+
-This book is a central repository of cheat sheets
-that are your ticket to becoming a data science expert.
+This repository serves as a hub for various Cheat Sheets related to
+the field of Data Science (with Python). What sets these Cheat Sheets apart is their
+multi-dimensional approach to enhancing the learning experience.
+Each Cheat Sheet is made available in three distinct formats: **PDF**, **Streamlit**, and **Google Colab**.
+
+This triad of accessibility ensures that learners can engage with the content in
+a way that suits their preferences and learning style.
+
+
+**Content**
+
+
+| Topic | PDF | Streamlit | Google Colab |
+|---------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| python | 
| 
| 
|
+| numpy | | | |
+| pandas | | | |
+| matplotlib | | | |
+| scikit-learn | | | |
+
+> **Note**: The PDF format cheat sheets included here are authored by other contributors and have been used as sources of inspiration for the content presented.
+
+## More Information
+
+### What is a Cheat Sheet ?
+
+A cheat sheet is a concise and informative reference guide that provides
+quick and easy access to essential information or instructions about a specific topic.
+
+It's designed to help individuals quickly understand key concepts, commands, formulas,
+or procedures without having to search through lengthy documentation or resources.
+Cheat sheets are often used as handy reference tools for tasks that require familiarity
+with specific details or steps, such as programming languages, software applications,
+or academic subjects. They serve as a valuable aid for both beginners and experienced
+practitioners by condensing important information into a single, easily digestible format.
+
+### What is Streamlit ?
+
+
+
+
+[Streamlit](https://streamlit.io/) is an open-source Python library that simplifies and accelerates the
+process of creating interactive web applications for data science and machine learning projects.
+It allows developers, data scientists, and researchers to transform data scripts into shareable
+web applications quickly and with minimal effort.
+
+
+
+
+### What is Jupyter Notebook ?
+
+
+
+
+[Jupyter Notebook](https://jupyter.org/) is an open-source web application that provides
+an interactive and flexible environment for creating, sharing, and executing documents that contain live code, equations, visualizations, and explanatory text. It's widely used by researchers, data scientists, educators, and professionals
+to develop and present code-based projects, analyses, and reports.
+
+
+
+
+### What is Google Colab ?
+
+
+
+
+[Google Colab](https://colab.research.google.com/), short for Google Colaboratory, is a cloud-based,
+interactive development environment provided by Google
+that enables users to write, execute, and share Python code in a
+collaborative and convenient manner. It's particularly popular among researchers,
+data scientists, and educators for its ease of use and the fact that it doesn't
+require any setup or installation.
+
+
+
+## References
+
+* [Data Science Cheat Sheets - DataCamp](https://www.datacamp.com/cheat-sheet)
+* [Cheatsheets and Handouts - Matplotlib](https://matplotlib.org/cheatsheets/)
+* [Streamlit Cheat Sheet - Daniel Lewis](https://github.com/daniellewisDL/streamlit-cheat-sheet)
-These cheat sheets are a path that quickly guides you through
-essential data science topics, including statistics, machine learning,
-and visualization.
-In addition, the examples are made with **Streamlit** and **Jupyter Notebook** (Google Colab) .
diff --git a/docs/examples/matplotlib/app.py b/docs/examples/matplotlib/app.py
new file mode 100644
index 0000000..119c35b
--- /dev/null
+++ b/docs/examples/matplotlib/app.py
@@ -0,0 +1,397 @@
+import streamlit as st
+from pathlib import Path
+import base64
+import requests
+
+
+# Initial page config
+st.set_page_config(
+ page_title='Matplotlib Cheat Sheet',
+ layout="wide",
+ initial_sidebar_state="expanded",
+)
+
+def main():
+ """
+ Main function to set up the Streamlit app layout.
+ """
+ cs_sidebar()
+ cs_body()
+ return None
+
+# Define img_to_bytes() function
+def img_to_bytes(img_url):
+ response = requests.get(img_url)
+ img_bytes = response.content
+ encoded = base64.b64encode(img_bytes).decode()
+ return encoded
+
+# Define the cs_sidebar() function
+def cs_sidebar():
+ """
+ Populate the sidebar with various content sections related to Matplotlib.
+ """
+ st.sidebar.markdown(
+ '''[
](https://streamlit.io/)'''.format(
+ img_to_bytes("https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/matplotlib/matplotlib.png")), unsafe_allow_html=True)
+
+ st.sidebar.header('Matplotlib Cheat Sheet')
+ st.sidebar.markdown('''
+[Matplotlib](https://matplotlib.org/) is a Python 2D plotting library which produces publication-quality
+figures in a variety of hardcopy formats and interactive environments across platforms.
+ ''', unsafe_allow_html=True)
+
+ # Matplotlib installation and import
+ st.sidebar.markdown('__Install and import Matplotlib__')
+ st.sidebar.code('$ pip install matplotlib')
+ st.sidebar.code('''
+# Import Matplotlib convention
+>>> import matplotlib.pyplot as plt
+''')
+
+ # Anatomy of a figure
+ st.sidebar.markdown('__Anatomy of a figure__')
+ st.sidebar.markdown(
+ '''[](https://streamlit.io/)'''.format(
+ img_to_bytes("https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/matplotlib/mlp_01.png")), unsafe_allow_html=True)
+
+ st.sidebar.markdown('''
+ In Matplotlib, a figure refers to the overall canvas or window that contains one or more individual plots or subplots.
+ Understanding the anatomy of a Matplotlib figure is crucial for creating and customizing your visualizations effectively.
+ ''', unsafe_allow_html=True)
+
+
+ # Example code for the workflow
+ st.sidebar.code('''
+ # Workflow
+ import matplotlib.pyplot as plt
+
+ # Step 1: Prepare Data
+ x = [1, 2, 3, 4]
+ y = [10, 20, 25, 30]
+
+ # Step 2: Create Plot
+ fig = plt.figure()
+ ax = fig.add_subplot(111)
+
+ # Step 3: Plot
+ ax.plot(x, y, color='lightblue', linewidth=3)
+
+ # Step 4: Customized Plot
+ ax.scatter([2, 4, 6], [5, 15, 25], color='darkgreen', marker='^')
+ ax.set_xlim(1, 6.5)
+
+ # Step 5: Save Plot
+ plt.savefig('foo.png')
+
+ # Step 6: Show Plot
+ plt.show()
+ ''')
+ return None
+
+
+# Define the cs_body() function
+def cs_body():
+ """
+ Create content sections for the main body of the Streamlit cheat sheet with NumPy examples.
+ """
+ col1, col2, col3 = st.columns(3) # Create columns for layout
+
+ #######################################
+ # COLUMN 1
+ #######################################
+
+ # Prepare the Data
+ col1.subheader('Basic Plots ')
+
+ ## Create a scatter plot
+ col1.code('''
+ # Create a scatter plot
+ X = np.random.uniform(0, 1, 100)
+ Y = np.random.uniform(0, 1, 100)
+ plt.scatter(X, Y)
+ ''')
+
+ ## Create a bar plot
+ col1.code('''
+ # Create a bar plot
+ X = np.arange(10)
+ Y = np.random.uniform(1, 10, 10)
+ plt.bar(X, Y)
+ ''')
+
+ ## Create an image plot using imshow
+ col1.code('''
+ # Create an image plot using imshow
+ Z = np.random.uniform(0, 1, (8, 8))
+ plt.imshow(Z)
+ ''')
+
+ ## Create a contour plot
+ col1.code('''
+ # Create a contour plot
+ Z = np.random.uniform(0, 1, (8, 8))
+ plt.contourf(Z)
+ plt.show()
+ ''')
+
+ ## Create a pie chart
+ col1.code('''
+ # Create a pie chart
+ Z = np.random.uniform(0, 1, 4)
+ plt.pie(Z)
+ ''')
+
+ ## Create a histogram
+ col1.code('''
+ # Create a histogram
+ Z = np.random.normal(0, 1, 100)
+ plt.hist(Z)
+ ''')
+
+ ## Create an error bar plot
+ col1.code('''
+ # Create an error bar plot
+ X = np.arange(5)
+ Y = np.random.uniform(0, 1, 5)
+ plt.errorbar(X, Y, Y / 4)
+ ''')
+
+ ## Create a box plot
+ col1.code('''
+ # Create a box plot
+ Z = np.random.normal(0, 1, (100, 3))
+ plt.boxplot(Z)
+ ''')
+
+ # Tweak
+ col1.subheader('Tweak')
+
+ ## Create a plot with a black solid line
+ col1.code('''
+ # Create a plot with a black solid line
+ X = np.linspace(0, 10, 100)
+ Y = np.sin(X)
+ plt.plot(X, Y, color="black")
+ ''')
+
+ ## Create a plot with a dashed line
+ col1.code('''
+ # Create a plot with a dashed line
+ X = np.linspace(0, 10, 100)
+ Y = np.sin(X)
+ plt.plot(X, Y, linestyle="--")
+ ''')
+
+ ## Create a plot with a thicker line
+ col1.code('''
+ # Create a plot with a thicker line
+ X = np.linspace(0, 10, 100)
+ Y = np.sin(X)
+ plt.plot(X, Y, linewidth=5)
+ ''')
+
+ ## Create a plot with markers
+ col1.code('''
+ # Create a plot with markers
+ X = np.linspace(0, 10, 100)
+ Y = np.sin(X)
+ plt.plot(X, Y, marker="o")
+ ''')
+
+ # Save
+ col1.subheader('Save')
+ col1.code('''
+ # Save the figure as a PNG file with higher resolution (300 dpi)
+ fig.savefig("my-first-figure.png", dpi=300)
+
+ # Save the figure as a PDF file
+ fig.savefig("my-first-figure.pdf")
+ ''')
+
+ #######################################
+ # COLUMN 2
+ #######################################
+
+ # Markers
+ col2.subheader('Organize')
+
+ ## Create a plot with two lines on the same axes
+ col2.code('''
+ # Create a plot with two lines on the same axes
+ X = np.linspace(0, 10, 100)
+ Y1, Y2 = np.sin(X), np.cos(X)
+ plt.plot(X, Y1, X, Y2)
+ ''')
+
+ ## Create a figure with two subplots (vertically stacked)
+ col2.code('''
+ # Create a figure with two subplots (vertically stacked)
+ X = np.linspace(0, 10, 100)
+ Y1, Y2 = np.sin(X), np.cos(X)
+ fig, (ax1, ax2) = plt.subplots(2, 1)
+ ax1.plot(X, Y1, color="C1")
+ ax2.plot(X, Y2, color="C0")
+ ''')
+
+ ## Create a figure with two subplots (horizontally aligned)
+ col2.code('''
+ # Create a figure with two subplots (horizontally aligned)
+ X = np.linspace(0, 10, 100)
+ Y1, Y2 = np.sin(X), np.cos(X)
+ fig, (ax1, ax2) = plt.subplots(1, 2)
+ ax1.plot(Y1, X, color="C1")
+ ax2.plot(Y2, X, color="C0")
+ ''')
+
+ # Label
+ col2.subheader('Label')
+
+ ## Create data and plot a sine wave
+ col2.code('''
+ # Create data and plot a sine wave
+ X = np.linspace(0, 10, 100)
+ Y = np.sin(X)
+ plt.plot(X, Y)
+ ''')
+
+ ## Modify plot properties
+ col2.code('''
+ # Modify plot properties
+ X = np.linspace(0, 10, 100)
+ Y = np.sin(X)
+ plt.plot(X, Y)
+ plt.title("A Sine wave")
+ plt.xlabel("Time")
+ plt.ylabel(None)
+ ''')
+
+ # Figure, axes & spines
+ col2.subheader('Figure, axes & spines')
+ col2.code('''
+ # Create a 3x3 grid of subplots
+ fig, axs = plt.subplots(3, 3)
+
+ # Set face colors for specific subplots
+ axs[0, 0].set_facecolor("#ddddff")
+ axs[2, 2].set_facecolor("#ffffdd")
+ ''')
+
+ col2.code('''
+ # Create a 3x3 grid of subplots
+ fig, axs = plt.subplots(3, 3)
+
+ # Add a grid specification and set face color for a specific subplot
+ gs = fig.add_gridspec(3, 3)
+ ax = fig.add_subplot(gs[0, :])
+ ax.set_facecolor("#ddddff")
+ ''')
+
+ col2.code('''
+ # Create a figure with a single subplot
+ fig, ax = plt.subplots()
+
+ # Remove top and right spines from the subplot
+ ax.spines["top"].set_color("None")
+ ax.spines["right"].set_color("None")
+ ''')
+
+ # Colors
+ col2.subheader('Colors')
+ col2.code('''
+ # Get a list of named colors
+ named_colors = plt.colormaps()
+ print("Colors:",named_colors)
+ ''')
+
+ #######################################
+ # COLUMN 3
+ #######################################
+
+ # Ticks & labels
+ col3.subheader('Ticks & labels')
+ col3.code('''
+ from matplotlib.ticker import MultipleLocator as ML
+ from matplotlib.ticker import ScalarFormatter as SF
+
+ # Create a figure with a single subplot
+ fig, ax = plt.subplots()
+
+ # Set minor tick locations and formatter for the x-axis
+ ax.xaxis.set_minor_locator(ML(0.2))
+ ax.xaxis.set_minor_formatter(SF())
+
+ # Rotate minor tick labels on the x-axis
+ ax.tick_params(axis='x', which='minor', rotation=90)
+ ''')
+
+ # Lines & markers
+ col3.subheader('Lines & markers')
+ col3.code('''
+ # Generate data and create a plot
+ X = np.linspace(0.1, 10 * np.pi, 1000)
+ Y = np.sin(X)
+ plt.plot(X, Y, "C1o:", markevery=25, mec="1.0")
+ ''')
+
+ # Scales & projections
+ col3.subheader('Scales & projections')
+ col3.code('''
+ # Create a figure with a single subplot
+ fig, ax = plt.subplots()
+
+ # Set x-axis scale to logarithmic
+ ax.set_xscale("log")
+
+ # Plot data with specified formatting
+ ax.plot(X, Y, "C1o-", markevery=25, mec="1.0")
+ ''')
+
+ # Text & ornaments
+ col3.subheader('Scales & projections')
+ col3.code('''
+ # Create a figure with a single subplot
+ fig, ax = plt.subplots()
+
+ # Fill the area between horizontal lines with a curve
+ ax.fill_betweenx([-1, 1], [0], [2*np.pi])
+
+ # Add a text annotation to the plot
+ ax.text(0, -1, r" Period $\Phi$")
+ ''')
+
+ # Legend
+ col3.subheader('Legend')
+ col3.code('''
+ # Create a figure with a single subplot
+ fig, ax = plt.subplots()
+
+ # Plot sine and cosine curves with specified colors and labels
+ ax.plot(X, np.sin(X), "C0", label="Sine")
+ ax.plot(X, np.cos(X), "C1", label="Cosine")
+
+ # Add a legend with customized positioning and formatting
+ ax.legend(bbox_to_anchor=(0, 1, 1, 0.1), ncol=2, mode="expand", loc="lower left")
+ ''')
+
+ # Annotation
+ col3.subheader('Annotation')
+ col3.code('''
+ # Create a figure with a single subplot
+ fig, ax = plt.subplots()
+
+ ax.plot(X, Y, "C1o:", markevery=25, mec="1.0")
+
+ # Add an annotation "A" with an arrow
+ ax.annotate("A", (X[250], Y[250]), (X[250], -1),
+ ha="center", va="center",
+ arrowprops={"arrowstyle": "->", "color": "C1"})
+ ''')
+
+
+
+
+
+# Run the main function if the script is executed directly
+if __name__ == '__main__':
+ main()
diff --git a/docs/examples/matplotlib/foo.png b/docs/examples/matplotlib/foo.png
new file mode 100644
index 0000000..2430cc7
Binary files /dev/null and b/docs/examples/matplotlib/foo.png differ
diff --git a/docs/examples/matplotlib/matplotlib.ipynb b/docs/examples/matplotlib/matplotlib.ipynb
new file mode 100644
index 0000000..576c94e
--- /dev/null
+++ b/docs/examples/matplotlib/matplotlib.ipynb
@@ -0,0 +1,1033 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "76c6497b",
+ "metadata": {},
+ "source": [
+ "![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
\n",
+ "\n",
+ "\n",
+ "# Matplotlib Cheat Sheet\n",
+ "\n",
+ "![\"numpy](\"https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/matplotlib/matplotlib.png\")
\n",
+ "\n",
+ "[Matplotlib](https://matplotlib.org/) is a Python 2D plotting library which produces publication-quality \n",
+ "figures in a variety of hardcopy formats and interactive environments across platforms."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5d810b35",
+ "metadata": {},
+ "source": [
+ "## Install and import Matplotlib\n",
+ "\n",
+ "`\n",
+ "$ pip install matplotlib\n",
+ "`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "ac095e9f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import matplotlib convention\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9643ef07",
+ "metadata": {},
+ "source": [
+ "## Anatomy of a figure\n",
+ "\n",
+ "![\"numpy](\"https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/matplotlib/mlp_01.png\")
\n",
+ "\n",
+ "In Matplotlib, a figure refers to the overall canvas or window that contains one or more individual plots or subplots. \n",
+ " Understanding the anatomy of a Matplotlib figure is crucial for creating and customizing your visualizations effectively."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "23d419fc",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "\n",
+ "\n",
+ " \n",
+ "# Matplotlib Cheat Sheet"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e99ef048",
+ "metadata": {},
+ "source": [
+ "![](\"https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/images/info_icon.png\"){kind=icon}
\n",
+ " ](https://streamlit.io/)'''.format(\n",
+ " img_to_bytes(\"https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/matplotlib/matplotlib.png\")), unsafe_allow_html=True)\n",
+ "\n",
+ " st.sidebar.header('Matplotlib Cheat Sheet')\n",
+ " st.sidebar.markdown('''\n",
+ "[Matplotlib](https://matplotlib.org/) is a Python 2D plotting library which produces publication-quality \n",
+ "figures in a variety of hardcopy formats and interactive environments across platforms.\n",
+ " ''', unsafe_allow_html=True)\n",
+ "\n",
+ " # Matplotlib installation and import\n",
+ " st.sidebar.markdown('__Install and import Matplotlib__')\n",
+ " st.sidebar.code('$ pip install matplotlib')\n",
+ " st.sidebar.code('''\n",
+ "# Import Matplotlib convention\n",
+ ">>> import matplotlib.pyplot as plt\n",
+ "''')\n",
+ "\n",
+ " # Anatomy of a figure\n",
+ " st.sidebar.markdown('__Anatomy of a figure__')\n",
+ " st.sidebar.markdown(\n",
+ " '''[](https://streamlit.io/)'''.format(\n",
+ " img_to_bytes(\"https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/matplotlib/mlp_01.png\")), unsafe_allow_html=True)\n",
+ "\n",
+ " st.sidebar.markdown('''\n",
+ " In Matplotlib, a figure refers to the overall canvas or window that contains one or more individual plots or subplots. \n",
+ " Understanding the anatomy of a Matplotlib figure is crucial for creating and customizing your visualizations effectively. \n",
+ " ''', unsafe_allow_html=True)\n",
+ "\n",
+ "\n",
+ " # Example code for the workflow\n",
+ " st.sidebar.code('''\n",
+ " # Workflow\n",
+ " import matplotlib.pyplot as plt\n",
+ " \n",
+ " # Step 1: Prepare Data\n",
+ " x = [1, 2, 3, 4] \n",
+ " y = [10, 20, 25, 30] \n",
+ "\n",
+ " # Step 2: Create Plot\n",
+ " fig = plt.figure()\n",
+ " ax = fig.add_subplot(111)\n",
+ "\n",
+ " # Step 3: Plot\n",
+ " ax.plot(x, y, color='lightblue', linewidth=3)\n",
+ "\n",
+ " # Step 4: Customized Plot\n",
+ " ax.scatter([2, 4, 6], [5, 15, 25], color='darkgreen', marker='^')\n",
+ " ax.set_xlim(1, 6.5)\n",
+ "\n",
+ " # Step 5: Save Plot\n",
+ " plt.savefig('foo.png')\n",
+ "\n",
+ " # Step 6: Show Plot\n",
+ " plt.show()\n",
+ " ''')\n",
+ " return None\n",
+ "\n",
+ "\n",
+ "# Define the cs_body() function\n",
+ "def cs_body():\n",
+ " \"\"\"\n",
+ " Create content sections for the main body of the Streamlit cheat sheet with NumPy examples.\n",
+ " \"\"\"\n",
+ " col1, col2, col3 = st.columns(3) # Create columns for layout\n",
+ "\n",
+ " #######################################\n",
+ " # COLUMN 1\n",
+ " #######################################\n",
+ "\n",
+ " # Prepare the Data\n",
+ " col1.subheader('Basic Plots ')\n",
+ "\n",
+ " ## Create a scatter plot\n",
+ " col1.code('''\n",
+ " # Create a scatter plot\n",
+ " X = np.random.uniform(0, 1, 100)\n",
+ " Y = np.random.uniform(0, 1, 100)\n",
+ " plt.scatter(X, Y)\n",
+ " ''')\n",
+ "\n",
+ " ## Create a bar plot\n",
+ " col1.code('''\n",
+ " # Create a bar plot\n",
+ " X = np.arange(10)\n",
+ " Y = np.random.uniform(1, 10, 10)\n",
+ " plt.bar(X, Y)\n",
+ " ''')\n",
+ "\n",
+ " ## Create an image plot using imshow\n",
+ " col1.code('''\n",
+ " # Create an image plot using imshow\n",
+ " Z = np.random.uniform(0, 1, (8, 8))\n",
+ " plt.imshow(Z)\n",
+ " ''')\n",
+ "\n",
+ " ## Create a contour plot\n",
+ " col1.code('''\n",
+ " # Create a contour plot\n",
+ " Z = np.random.uniform(0, 1, (8, 8))\n",
+ " plt.contourf(Z)\n",
+ " plt.show()\n",
+ " ''')\n",
+ "\n",
+ " ## Create a pie chart\n",
+ " col1.code('''\n",
+ " # Create a pie chart\n",
+ " Z = np.random.uniform(0, 1, 4)\n",
+ " plt.pie(Z)\n",
+ " ''')\n",
+ "\n",
+ " ## Create a histogram\n",
+ " col1.code('''\n",
+ " # Create a histogram\n",
+ " Z = np.random.normal(0, 1, 100)\n",
+ " plt.hist(Z)\n",
+ " ''')\n",
+ "\n",
+ " ## Create an error bar plot\n",
+ " col1.code('''\n",
+ " # Create an error bar plot\n",
+ " X = np.arange(5)\n",
+ " Y = np.random.uniform(0, 1, 5)\n",
+ " plt.errorbar(X, Y, Y / 4)\n",
+ " ''')\n",
+ "\n",
+ " ## Create a box plot\n",
+ " col1.code('''\n",
+ " # Create a box plot\n",
+ " Z = np.random.normal(0, 1, (100, 3))\n",
+ " plt.boxplot(Z)\n",
+ " ''')\n",
+ "\n",
+ " # Tweak\n",
+ " col1.subheader('Tweak')\n",
+ "\n",
+ " ## Create a plot with a black solid line\n",
+ " col1.code('''\n",
+ " # Create a plot with a black solid line\n",
+ " X = np.linspace(0, 10, 100)\n",
+ " Y = np.sin(X)\n",
+ " plt.plot(X, Y, color=\"black\")\n",
+ " ''')\n",
+ "\n",
+ " ## Create a plot with a dashed line\n",
+ " col1.code('''\n",
+ " # Create a plot with a dashed line\n",
+ " X = np.linspace(0, 10, 100)\n",
+ " Y = np.sin(X)\n",
+ " plt.plot(X, Y, linestyle=\"--\")\n",
+ " ''')\n",
+ "\n",
+ " ## Create a plot with a thicker line\n",
+ " col1.code('''\n",
+ " # Create a plot with a thicker line\n",
+ " X = np.linspace(0, 10, 100)\n",
+ " Y = np.sin(X)\n",
+ " plt.plot(X, Y, linewidth=5)\n",
+ " ''')\n",
+ "\n",
+ " ## Create a plot with markers\n",
+ " col1.code('''\n",
+ " # Create a plot with markers\n",
+ " X = np.linspace(0, 10, 100)\n",
+ " Y = np.sin(X)\n",
+ " plt.plot(X, Y, marker=\"o\")\n",
+ " ''')\n",
+ "\n",
+ " # Save\n",
+ " col1.subheader('Save')\n",
+ " col1.code('''\n",
+ " # Save the figure as a PNG file with higher resolution (300 dpi)\n",
+ " fig.savefig(\"my-first-figure.png\", dpi=300)\n",
+ "\n",
+ " # Save the figure as a PDF file\n",
+ " fig.savefig(\"my-first-figure.pdf\")\n",
+ " ''')\n",
+ "\n",
+ " #######################################\n",
+ " # COLUMN 2\n",
+ " #######################################\n",
+ "\n",
+ " # Markers\n",
+ " col2.subheader('Organize')\n",
+ "\n",
+ " ## Create a plot with two lines on the same axes\n",
+ " col2.code('''\n",
+ " # Create a plot with two lines on the same axes\n",
+ " X = np.linspace(0, 10, 100)\n",
+ " Y1, Y2 = np.sin(X), np.cos(X)\n",
+ " plt.plot(X, Y1, X, Y2)\n",
+ " ''')\n",
+ "\n",
+ " ## Create a figure with two subplots (vertically stacked)\n",
+ " col2.code('''\n",
+ " # Create a figure with two subplots (vertically stacked)\n",
+ " X = np.linspace(0, 10, 100)\n",
+ " Y1, Y2 = np.sin(X), np.cos(X)\n",
+ " fig, (ax1, ax2) = plt.subplots(2, 1)\n",
+ " ax1.plot(X, Y1, color=\"C1\")\n",
+ " ax2.plot(X, Y2, color=\"C0\")\n",
+ " ''')\n",
+ "\n",
+ " ## Create a figure with two subplots (horizontally aligned)\n",
+ " col2.code('''\n",
+ " # Create a figure with two subplots (horizontally aligned)\n",
+ " X = np.linspace(0, 10, 100)\n",
+ " Y1, Y2 = np.sin(X), np.cos(X)\n",
+ " fig, (ax1, ax2) = plt.subplots(1, 2)\n",
+ " ax1.plot(Y1, X, color=\"C1\")\n",
+ " ax2.plot(Y2, X, color=\"C0\")\n",
+ " ''')\n",
+ "\n",
+ " # Label\n",
+ " col2.subheader('Label')\n",
+ "\n",
+ " ## Create data and plot a sine wave\n",
+ " col2.code('''\n",
+ " # Create data and plot a sine wave\n",
+ " X = np.linspace(0, 10, 100)\n",
+ " Y = np.sin(X)\n",
+ " plt.plot(X, Y)\n",
+ " ''')\n",
+ "\n",
+ " ## Modify plot properties\n",
+ " col2.code('''\n",
+ " # Modify plot properties\n",
+ " X = np.linspace(0, 10, 100)\n",
+ " Y = np.sin(X)\n",
+ " plt.plot(X, Y)\n",
+ " plt.title(\"A Sine wave\")\n",
+ " plt.xlabel(\"Time\")\n",
+ " plt.ylabel(None)\n",
+ " ''')\n",
+ "\n",
+ " # Figure, axes & spines\n",
+ " col2.subheader('Figure, axes & spines')\n",
+ " col2.code('''\n",
+ " # Create a 3x3 grid of subplots\n",
+ " fig, axs = plt.subplots(3, 3)\n",
+ "\n",
+ " # Set face colors for specific subplots\n",
+ " axs[0, 0].set_facecolor(\"#ddddff\")\n",
+ " axs[2, 2].set_facecolor(\"#ffffdd\")\n",
+ " ''')\n",
+ "\n",
+ " col2.code('''\n",
+ " # Create a 3x3 grid of subplots\n",
+ " fig, axs = plt.subplots(3, 3)\n",
+ "\n",
+ " # Add a grid specification and set face color for a specific subplot\n",
+ " gs = fig.add_gridspec(3, 3)\n",
+ " ax = fig.add_subplot(gs[0, :])\n",
+ " ax.set_facecolor(\"#ddddff\")\n",
+ " ''')\n",
+ "\n",
+ " col2.code('''\n",
+ " # Create a figure with a single subplot\n",
+ " fig, ax = plt.subplots()\n",
+ "\n",
+ " # Remove top and right spines from the subplot\n",
+ " ax.spines[\"top\"].set_color(\"None\")\n",
+ " ax.spines[\"right\"].set_color(\"None\")\n",
+ " ''')\n",
+ "\n",
+ " # Colors\n",
+ " col2.subheader('Colors')\n",
+ " col2.code('''\n",
+ " # Get a list of named colors\n",
+ " named_colors = plt.colormaps() \n",
+ " print(\"Colors:\",named_colors)\n",
+ " ''')\n",
+ "\n",
+ " #######################################\n",
+ " # COLUMN 3\n",
+ " #######################################\n",
+ "\n",
+ " # Ticks & labels\n",
+ " col3.subheader('Ticks & labels')\n",
+ " col3.code('''\n",
+ " from matplotlib.ticker import MultipleLocator as ML\n",
+ " from matplotlib.ticker import ScalarFormatter as SF\n",
+ "\n",
+ " # Create a figure with a single subplot\n",
+ " fig, ax = plt.subplots()\n",
+ " \n",
+ " # Set minor tick locations and formatter for the x-axis\n",
+ " ax.xaxis.set_minor_locator(ML(0.2))\n",
+ " ax.xaxis.set_minor_formatter(SF())\n",
+ " \n",
+ " # Rotate minor tick labels on the x-axis\n",
+ " ax.tick_params(axis='x', which='minor', rotation=90)\n",
+ " ''')\n",
+ "\n",
+ " # Lines & markers\n",
+ " col3.subheader('Lines & markers')\n",
+ " col3.code('''\n",
+ " # Generate data and create a plot\n",
+ " X = np.linspace(0.1, 10 * np.pi, 1000)\n",
+ " Y = np.sin(X)\n",
+ " plt.plot(X, Y, \"C1o:\", markevery=25, mec=\"1.0\")\n",
+ " ''')\n",
+ "\n",
+ " # Scales & projections\n",
+ " col3.subheader('Scales & projections')\n",
+ " col3.code('''\n",
+ " # Create a figure with a single subplot\n",
+ " fig, ax = plt.subplots()\n",
+ " \n",
+ " # Set x-axis scale to logarithmic\n",
+ " ax.set_xscale(\"log\")\n",
+ " \n",
+ " # Plot data with specified formatting\n",
+ " ax.plot(X, Y, \"C1o-\", markevery=25, mec=\"1.0\")\n",
+ " ''')\n",
+ "\n",
+ " # Text & ornaments\n",
+ " col3.subheader('Scales & projections')\n",
+ " col3.code('''\n",
+ " # Create a figure with a single subplot\n",
+ " fig, ax = plt.subplots()\n",
+ " \n",
+ " # Fill the area between horizontal lines with a curve\n",
+ " ax.fill_betweenx([-1, 1], [0], [2*np.pi])\n",
+ " \n",
+ " # Add a text annotation to the plot\n",
+ " ax.text(0, -1, r\" Period $\\Phi$\")\n",
+ " ''')\n",
+ "\n",
+ " # Legend\n",
+ " col3.subheader('Legend')\n",
+ " col3.code('''\n",
+ " # Create a figure with a single subplot\n",
+ " fig, ax = plt.subplots()\n",
+ " \n",
+ " # Plot sine and cosine curves with specified colors and labels\n",
+ " ax.plot(X, np.sin(X), \"C0\", label=\"Sine\")\n",
+ " ax.plot(X, np.cos(X), \"C1\", label=\"Cosine\")\n",
+ " \n",
+ " # Add a legend with customized positioning and formatting\n",
+ " ax.legend(bbox_to_anchor=(0, 1, 1, 0.1), ncol=2, mode=\"expand\", loc=\"lower left\")\n",
+ " ''')\n",
+ "\n",
+ " # Annotation\n",
+ " col3.subheader('Annotation')\n",
+ " col3.code('''\n",
+ " # Create a figure with a single subplot\n",
+ " fig, ax = plt.subplots()\n",
+ " \n",
+ " ax.plot(X, Y, \"C1o:\", markevery=25, mec=\"1.0\")\n",
+ " \n",
+ " # Add an annotation \"A\" with an arrow\n",
+ " ax.annotate(\"A\", (X[250], Y[250]), (X[250], -1),\n",
+ " ha=\"center\", va=\"center\",\n",
+ " arrowprops={\"arrowstyle\": \"->\", \"color\": \"C1\"})\n",
+ " ''')\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "# Run the main function if the script is executed directly\n",
+ "if __name__ == '__main__':\n",
+ " main()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "df712ce3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!streamlit run app.py & npx localtunnel --port 8501"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/numpy/app.py b/docs/examples/numpy/app.py
index d3c550e..2947a49 100644
--- a/docs/examples/numpy/app.py
+++ b/docs/examples/numpy/app.py
@@ -6,7 +6,7 @@
# Initial page config
st.set_page_config(
- page_title='Streamlit cheat sheet',
+ page_title='NumPy Cheat Sheet',
layout="wide",
initial_sidebar_state="expanded",
)
@@ -37,7 +37,7 @@ def cs_sidebar():
st.sidebar.header('NumPy Cheat Sheet')
st.sidebar.markdown('''
-The [NumPy](https://numpy.org/) library is the core library for scientific computing in
+[NumPy](https://numpy.org/) is the core library for scientific computing in
Python. It provides a high-performance multidimensional array
object, and tools for working with these arrays.
''', unsafe_allow_html=True)
diff --git a/docs/examples/numpy/numpy.ipynb b/docs/examples/numpy/numpy.ipynb
new file mode 100644
index 0000000..dd9a962
--- /dev/null
+++ b/docs/examples/numpy/numpy.ipynb
@@ -0,0 +1,1147 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "13773235",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "\n",
+ "# NumPy Cheat Sheet\n",
+ "\n",
+ "![\"numpy](\"https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/numpy/numpy2.png\")
\n",
+ "\n",
+ "[NumPy](https://numpy.org/) is the core library for scientific computing in\n",
+ "Python. It provides a high-performance multidimensional array\n",
+ "object, and tools for working with these arrays.\n",
+ "\n",
+ "## Install and import NumPy\n",
+ "\n",
+ "`\n",
+ "$ pip install numpy\n",
+ "`\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "a5cbafb3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import NumPy convention\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "47a84eb1",
+ "metadata": {},
+ "source": [
+ "## NumPy Arrays\n",
+ "\n",
+ "![\"numpy](\"https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/numpy/np_01.png\")
\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "2fbcda2e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Array a:\n",
+ "[1 2 3]\n",
+ "\n",
+ "Array b:\n",
+ "[[1.5 2. 3. ]\n",
+ " [4. 5. 6. ]]\n",
+ "\n",
+ "Array c:\n",
+ "[[[1.5 2. 3. ]\n",
+ " [4. 5. 6. ]]\n",
+ "\n",
+ " [[3. 2. 1. ]\n",
+ " [4. 5. 6. ]]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Create a 1D array\n",
+ "a = np.array([1, 2, 3])\n",
+ "\n",
+ "# Create a 2D array with specified dtype\n",
+ "b = np.array([\n",
+ " (1.5, 2, 3),\n",
+ " (4, 5, 6)\n",
+ "], dtype=float)\n",
+ "\n",
+ "# Create a 3D array with specified dtype\n",
+ "c = np.array([\n",
+ " [(1.5, 2, 3), (4, 5, 6)],\n",
+ " [(3, 2, 1), (4, 5, 6)]\n",
+ "], dtype=float)\n",
+ "\n",
+ "print(\"Array a:\")\n",
+ "print(a)\n",
+ "\n",
+ "print(\"\\nArray b:\")\n",
+ "print(b)\n",
+ "\n",
+ "print(\"\\nArray c:\")\n",
+ "print(c)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f8be45d5",
+ "metadata": {},
+ "source": [
+ "## Initial Placeholders\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "811b4aa3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "zeros_arr:\n",
+ "[[0. 0. 0. 0.]\n",
+ " [0. 0. 0. 0.]\n",
+ " [0. 0. 0. 0.]]\n",
+ "\n",
+ "ones_arr:\n",
+ "[[[1. 1. 1. 1.]\n",
+ " [1. 1. 1. 1.]\n",
+ " [1. 1. 1. 1.]]\n",
+ "\n",
+ " [[1. 1. 1. 1.]\n",
+ " [1. 1. 1. 1.]\n",
+ " [1. 1. 1. 1.]]]\n",
+ "\n",
+ "d:\n",
+ "[10 15 20]\n",
+ "\n",
+ "e:\n",
+ "[0. 0.25 0.5 0.75 1. 1.25 1.5 1.75 2. ]\n",
+ "\n",
+ "f:\n",
+ "[[7 7]\n",
+ " [7 7]]\n",
+ "\n",
+ "g:\n",
+ "[[1. 0.]\n",
+ " [0. 1.]]\n",
+ "\n",
+ "random_arr:\n",
+ "[[0.00439382 0.02702873]\n",
+ " [0.19578698 0.34798592]]\n",
+ "\n",
+ "empty_arr:\n",
+ "[[1.5 2. ]\n",
+ " [3. 4. ]\n",
+ " [5. 6. ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Create an array of zeros\n",
+ "zeros_arr = np.zeros((3, 4))\n",
+ "\n",
+ "# Create an array of ones\n",
+ "ones_arr = np.ones((2, 3, 4))\n",
+ "\n",
+ "# Create an array of evenly spaced values (step value)\n",
+ "d = np.arange(10, 25, 5)\n",
+ "\n",
+ "# Create an array of evenly spaced values (number of samples)\n",
+ "e = np.linspace(0, 2, 9)\n",
+ "\n",
+ "# Create a constant array\n",
+ "f = np.full((2, 2), 7)\n",
+ "\n",
+ "# Create a 2x2 identity matrix\n",
+ "g = np.eye(2)\n",
+ "\n",
+ "# Create an array with random values\n",
+ "random_arr = np.random.random((2, 2))\n",
+ "\n",
+ "# Create an empty array\n",
+ "empty_arr = np.empty((3, 2))\n",
+ "\n",
+ "print(\"zeros_arr:\")\n",
+ "print(zeros_arr)\n",
+ "\n",
+ "print(\"\\nones_arr:\")\n",
+ "print(ones_arr)\n",
+ "\n",
+ "print(\"\\nd:\")\n",
+ "print(d)\n",
+ "\n",
+ "print(\"\\ne:\")\n",
+ "print(e)\n",
+ "\n",
+ "print(\"\\nf:\")\n",
+ "print(f)\n",
+ "\n",
+ "print(\"\\ng:\")\n",
+ "print(g)\n",
+ "\n",
+ "print(\"\\nrandom_arr:\")\n",
+ "print(random_arr)\n",
+ "\n",
+ "print(\"\\nempty_arr:\")\n",
+ "print(empty_arr)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3be9b36a",
+ "metadata": {},
+ "source": [
+ "## NumPy Data Types\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "ca170aba",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "int64_type: \n",
+ "\n",
+ " \n",
+ "# Numpy Cheat Sheet"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3a0dbe5e",
+ "metadata": {},
+ "source": [
+ "\n",
+ " ](https://streamlit.io/)'''.format(\n",
+ " img_to_bytes(\"https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/numpy/numpy2.png\")), unsafe_allow_html=True)\n",
+ "\n",
+ " st.sidebar.header('NumPy Cheat Sheet')\n",
+ " st.sidebar.markdown('''\n",
+ "[NumPy](https://numpy.org/) is the core library for scientific computing in\n",
+ "Python. It provides a high-performance multidimensional array\n",
+ "object, and tools for working with these arrays.\n",
+ " ''', unsafe_allow_html=True)\n",
+ "\n",
+ " # NumPy installation and import\n",
+ " st.sidebar.markdown('__Install and import NumPy__')\n",
+ " st.sidebar.code('$ pip install numpy')\n",
+ " st.sidebar.code('''\n",
+ "# Import NumPy convention\n",
+ ">>> import numpy as np\n",
+ "''')\n",
+ "\n",
+ " # NumPy array creation\n",
+ " st.sidebar.markdown('__NumPy Arrays__')\n",
+ " st.sidebar.markdown(\n",
+ " '''[](https://streamlit.io/)'''.format(\n",
+ " img_to_bytes(\"https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/numpy/np_02.png\")), unsafe_allow_html=True)\n",
+ "\n",
+ " st.sidebar.code('''\n",
+ " # Create a 1D array\n",
+ " a = np.array([1, 2, 3])\n",
+ "\n",
+ " # Create a 2D array with specified dtype\n",
+ " b = np.array([\n",
+ " (1.5, 2, 3),\n",
+ " (4, 5, 6)\n",
+ " ], dtype=float)\n",
+ "\n",
+ " # Create a 3D array with specified dtype\n",
+ " c = np.array([\n",
+ " [(1.5, 2, 3), (4, 5, 6)], \n",
+ " [(3, 2, 1), (4, 5, 6)]\n",
+ " ], dtype=float)\n",
+ " ''')\n",
+ " return None\n",
+ "\n",
+ "\n",
+ "# Define the cs_body() function\n",
+ "def cs_body():\n",
+ " \"\"\"\n",
+ " Create content sections for the main body of the Streamlit cheat sheet with NumPy examples.\n",
+ " \"\"\"\n",
+ " col1, col2, col3 = st.columns(3) # Create columns for layout\n",
+ "\n",
+ " #######################################\n",
+ " # COLUMN 1\n",
+ " #######################################\n",
+ "\n",
+ " # Initial Placeholders\n",
+ " col1.subheader('Initial Placeholders')\n",
+ " col1.code('''\n",
+ " # Create an array of zeros\n",
+ " zeros_arr = np.zeros((3, 4))\n",
+ "\n",
+ " # Create an array of ones\n",
+ " ones_arr = np.ones((2, 3, 4))\n",
+ "\n",
+ " # Create an array of evenly spaced values (step value)\n",
+ " d = np.arange(10, 25, 5)\n",
+ "\n",
+ " # Create an array of evenly spaced values (number of samples)\n",
+ " e = np.linspace(0, 2, 9)\n",
+ "\n",
+ " # Create a constant array\n",
+ " f = np.full((2, 2), 7)\n",
+ "\n",
+ " # Create a 2X2 identity matrix\n",
+ " g = np.eye(2)\n",
+ "\n",
+ " # Create an array with random values\n",
+ " random_arr = np.random.random((2, 2))\n",
+ "\n",
+ " # Create an empty array\n",
+ " empty_arr = np.empty((3, 2))\n",
+ " ''')\n",
+ "\n",
+ " # Saving & Loading On Disk\n",
+ " col1.subheader('Saving & Loading On Disk')\n",
+ " col1.code('''\n",
+ " # Save a NumPy array to a file\n",
+ " a = np.array([1, 2, 3])\n",
+ " np.save('my_array', a)\n",
+ "\n",
+ " # Save multiple NumPy arrays to a compressed file\n",
+ " b = np.array([\n",
+ " (1.5, 2, 3), \n",
+ " (4, 5, 6)\n",
+ " ], dtype=float)\n",
+ " np.savez('array.npz', a, b)\n",
+ "\n",
+ " # Load a NumPy array from a file\n",
+ " loaded_array = np.load('my_array.npy')\n",
+ " ''')\n",
+ "\n",
+ " # Saving & Loading Text Files\n",
+ " col1.subheader('Saving & Loading Text Files')\n",
+ " col1.code('''\n",
+ " # Load data from a text file\n",
+ " loaded_txt = np.loadtxt(\"myfile.txt\")\n",
+ "\n",
+ " # Load data from a CSV file with specified delimiter\n",
+ " loaded_csv = np.genfromtxt(\n",
+ " \"my_file.csv\",\n",
+ " delimiter=',')\n",
+ "\n",
+ " # Save a NumPy array to a text file\n",
+ " a = np.array([1, 2, 3])\n",
+ " np.savetxt(\n",
+ " \"myarray.txt\", \n",
+ " a, \n",
+ " delimiter=\" \")\n",
+ " ''')\n",
+ "\n",
+ " # NumPy data types\n",
+ " col1.subheader('NumPy Data Types')\n",
+ " col1.code('''\n",
+ " # Signed 64-bit integer types\n",
+ " int64_type = np.int64\n",
+ "\n",
+ " # Standard double-precision floating point\n",
+ " float32_type = np.float32\n",
+ "\n",
+ " # Complex numbers represented by 128 floats\n",
+ " complex_type = np.complex128\n",
+ "\n",
+ " # Boolean type storing TRUE and FALSE values\n",
+ " bool_type = np.bool_\n",
+ "\n",
+ " # Python object type\n",
+ " object_type = np.object_\n",
+ "\n",
+ " # Fixed-length string type\n",
+ " string_type = np.string_\n",
+ "\n",
+ " # Fixed-length unicode type\n",
+ " unicode_type = np.unicode_\n",
+ " ''')\n",
+ "\n",
+ "\n",
+ "\n",
+ " # Asking for help\n",
+ " col1.subheader('Asking for Help')\n",
+ " col1.code('''\n",
+ " # Get information about a NumPy function or object\n",
+ " np.info(np.ndarray.dtype)\n",
+ " ''')\n",
+ "\n",
+ " #######################################\n",
+ " # COLUMN 2\n",
+ " #######################################\n",
+ "\n",
+ " # Inspecting array properties\n",
+ " col2.subheader('Inspecting Array Properties')\n",
+ " col2.code('''\n",
+ " # Array dimensions\n",
+ " a_shape = a.shape\n",
+ "\n",
+ " # Length of array\n",
+ " a_length = len(a)\n",
+ "\n",
+ " # Number of array dimensions\n",
+ " b_ndim = b.ndim\n",
+ "\n",
+ " # Number of array elements\n",
+ " e_size = e.size\n",
+ "\n",
+ " # Data type of array elements\n",
+ " b_dtype = b.dtype\n",
+ "\n",
+ " # Name of data type\n",
+ " b_dtype_name = b.dtype.name\n",
+ "\n",
+ " # Convert an array to a different type\n",
+ " b_as_int = b.astype(int)\n",
+ " ''')\n",
+ "\n",
+ "\n",
+ "\n",
+ " # Arithmetic operations\n",
+ " col2.subheader('Arithmetic Operations')\n",
+ " col2.code('''\n",
+ " # Subtraction\n",
+ " subtraction_result = a - b\n",
+ " subtraction_np = np.subtract(a, b)\n",
+ "\n",
+ " # Addition\n",
+ " addition_result = b + a\n",
+ " addition_np = np.add(b, a)\n",
+ "\n",
+ " # Division\n",
+ " division_result = a / b\n",
+ " division_np = np.divide(a, b)\n",
+ "\n",
+ " # Multiplication\n",
+ " multiplication_result = a * b\n",
+ " multiplication_np = np.multiply(a, b)\n",
+ "\n",
+ " # Exponentiation\n",
+ " exponentiation_result = np.exp(b)\n",
+ "\n",
+ " # Square root\n",
+ " sqrt_result = np.sqrt(b)\n",
+ "\n",
+ " # Sine of an array\n",
+ " sin_result = np.sin(a)\n",
+ "\n",
+ " # Element-wise cosine\n",
+ " cos_result = np.cos(b)\n",
+ "\n",
+ " # Element-wise natural logarithm\n",
+ " log_result = np.log(a)\n",
+ "\n",
+ " # Dot product\n",
+ " dot_product_result = e.dot(f)\n",
+ " ''')\n",
+ "\n",
+ "\n",
+ " # Aggregate functions\n",
+ " col2.subheader('Aggregate Functions')\n",
+ " col2.code('''\n",
+ " # Array-wise sum\n",
+ " array_sum = a.sum()\n",
+ "\n",
+ " # Array-wise minimum value\n",
+ " array_min = a.min()\n",
+ "\n",
+ " # Maximum value of an array row\n",
+ " row_max = b.max(axis=0)\n",
+ "\n",
+ " # Cumulative sum of the elements\n",
+ " cumulative_sum = b.cumsum(axis=1)\n",
+ "\n",
+ " # Mean\n",
+ " array_mean = a.mean()\n",
+ "\n",
+ " # Median\n",
+ " array_median = b.median()\n",
+ "\n",
+ " # Correlation coefficient\n",
+ " corr_coefficient = a.corrcoef()\n",
+ "\n",
+ " # Standard deviation\n",
+ " std_deviation = np.std(b)\n",
+ " ''')\n",
+ "\n",
+ " #######################################\n",
+ " # COLUMN 3\n",
+ " #######################################\n",
+ "\n",
+ " # Comparison operations\n",
+ " col3.subheader('Comparison Operations')\n",
+ " col3.code('''\n",
+ " # Element-wise comparison for equality\n",
+ " equality_comparison = a == b\n",
+ "\n",
+ " # Element-wise comparison for less than\n",
+ " less_than_comparison = a < 2\n",
+ "\n",
+ " # Array-wise comparison using np.array_equal\n",
+ " np_equal = np.array_equal(a, b)\n",
+ " ''')\n",
+ "\n",
+ " # Copying arrays\n",
+ " col3.subheader('Copying Arrays')\n",
+ " col3.code('''\n",
+ " # Create a view of the array with the same data\n",
+ " array_view = a.view()\n",
+ "\n",
+ " # Create a copy of the array\n",
+ " array_copy = np.copy(a)\n",
+ "\n",
+ " # Create a deep copy of the array\n",
+ " array_deep_copy = a.copy()\n",
+ " ''')\n",
+ "\n",
+ " # Sorting arrays\n",
+ " col3.subheader('Sorting Arrays')\n",
+ " col3.code('''\n",
+ " # Sort an array\n",
+ " a.sort()\n",
+ "\n",
+ " # Sort the elements of an array's axis\n",
+ " c.sort(axis=0)\n",
+ " ''')\n",
+ "\n",
+ "\n",
+ " # Subsetting, Slicing, and Indexing\n",
+ " col3.subheader('Subsetting, Slicing, and Indexing')\n",
+ " col3.code('''\n",
+ " # Subsetting\n",
+ " element_at_2nd_index = a[2] \n",
+ "\n",
+ " # Select the element at row 1, column 2\n",
+ " element_row_1_col_2 = b[1, 2] \n",
+ "\n",
+ " # Slicing\n",
+ " sliced_a = a[0:2]\n",
+ "\n",
+ " # Select items at rows 0 and 1 in column 1\n",
+ " sliced_b = b[0:2, 1]\n",
+ "\n",
+ " # Select all items at row 0\n",
+ " sliced_c = b[:1] \n",
+ "\n",
+ " # Reversed array\n",
+ " reversed_a = a[::-1] \n",
+ "\n",
+ " # Boolean Indexing\n",
+ " a_less_than_2 = a[a < 2]\n",
+ "\n",
+ " # Fancy Indexing\n",
+ " fancy_indexing_result = b[ \n",
+ " [1, 0, 1, 0], \n",
+ " [0, 1, 2, 0]\n",
+ " ] # array([ 4. , 2. , 6. , 1.5])\n",
+ " fancy_indexing_subset = b[[1, 0, 1, 0]][:, [0, 1, 2, 0]] \n",
+ " ''')\n",
+ "\n",
+ " # Array Manipulation\n",
+ " col3.subheader('Array Manipulation')\n",
+ " col3.code('''\n",
+ " # Transposing Array\n",
+ " transposed_b = np.transpose(b)\n",
+ " transposed_b_T = transposed_b.T\n",
+ "\n",
+ " # Changing Array Shape\n",
+ " flattened_h = h.ravel()\n",
+ " reshaped_g = g.reshape(3, -2)\n",
+ "\n",
+ " # Adding/Removing Elements\n",
+ " resized_h = np.resize(h, (2, 6)) # Using np.resize to avoid the error\n",
+ " appended_array = np.append(h, g)\n",
+ " inserted_array = np.insert(a, 1, 5)\n",
+ " deleted_array = np.delete(a, [1])\n",
+ "\n",
+ " # Combining Arrays\n",
+ " concatenated_arrays = np.concatenate((a, d), axis=0)\n",
+ " vstacked_arrays = np.vstack((a, b))\n",
+ " hstacked_arrays = np.hstack((e, f))\n",
+ " column_stacked_arrays = np.column_stack((a, d))\n",
+ " c_stacked_arrays = np.c_[a, d]\n",
+ "\n",
+ " # Splitting Arrays\n",
+ " hsplit_array = np.hsplit(a, 3)\n",
+ " vsplit_array = np.vsplit(c, 2)\n",
+ " ''')\n",
+ "\n",
+ "# Run the main function if the script is executed directly\n",
+ "if __name__ == '__main__':\n",
+ " main()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6224f2fb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!streamlit run app.py & npx localtunnel --port 8501"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/pandas/app.py b/docs/examples/pandas/app.py
new file mode 100644
index 0000000..f0f9e6b
--- /dev/null
+++ b/docs/examples/pandas/app.py
@@ -0,0 +1,330 @@
+import streamlit as st
+from pathlib import Path
+import base64
+import requests
+
+# Initial page config
+st.set_page_config(
+ page_title='Pandas Cheat Sheet',
+ layout="wide",
+ initial_sidebar_state="expanded",
+)
+
+def main():
+ """
+ Main function to set up the Streamlit app layout.
+ """
+ cs_sidebar()
+ cs_body()
+ return None
+
+# Define img_to_bytes() function
+def img_to_bytes(img_url):
+ response = requests.get(img_url)
+ img_bytes = response.content
+ encoded = base64.b64encode(img_bytes).decode()
+ return encoded
+
+
+# Define the cs_sidebar() function
+def cs_sidebar():
+ """
+ Populate the sidebar with various content sections related to Pandas.
+ """
+ st.sidebar.markdown(
+ '''[](https://streamlit.io/)'''.format(
+ img_to_bytes("https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/pandas/pandas.png")), unsafe_allow_html=True)
+
+ st.sidebar.header('Pandas Cheat Sheet')
+ st.sidebar.markdown('''
+[Pandas](https://pandas.pydata.org/) is built on NumPy and provides easy-to-use
+data structures and data analysis tools for the Python
+programming language.
+ ''', unsafe_allow_html=True)
+
+ # Pandas installation and import
+ st.sidebar.markdown('__Install and import Pandas__')
+ st.sidebar.code('$ pip install pandas')
+ st.sidebar.code('''
+# Import Pandas convention
+>>> import pandas as pd
+''')
+
+ # Pandas array creation
+ st.sidebar.subheader('Pandas Data Structures')
+ st.sidebar.markdown('__Series__')
+ st.sidebar.markdown(
+ '''[](https://streamlit.io/)'''.format(
+ img_to_bytes("https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/pandas/serie.png")), unsafe_allow_html=True)
+
+ st.sidebar.markdown('''
+ A **one-dimensional** labeled array a capable of holding any data type.
+ ''', unsafe_allow_html=True)
+
+ st.sidebar.code('''
+ # Create a pandas Series
+ s = pd.Series(
+ [3, -5, 7, 4],
+ index=['a', 'b', 'c', 'd']
+ )
+ ''')
+
+ st.sidebar.markdown('__DataFrame__')
+ st.sidebar.markdown('''
+ A **two-dimensional** labeled data structure with columns of potentially different types.
+ ''', unsafe_allow_html=True)
+ st.sidebar.markdown(
+ '''[](https://streamlit.io/)'''.format(
+ img_to_bytes("https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/pandas/df.png")), unsafe_allow_html=True)
+
+ st.sidebar.code('''
+ # Create a pandas DataFrame
+ data = {
+ 'Country': ['Belgium', 'India', 'Brazil'],
+ 'Capital': ['Brussels', 'New Delhi', 'Brasília'],
+ 'Population': [11190846, 1303171035, 207847528]
+ }
+ df = pd.DataFrame(
+ data,
+ columns=['Country', 'Capital', 'Population']
+ )
+ ''')
+
+ return None
+
+
+# Define the cs_body() function
+def cs_body():
+ """
+ Create content sections for the main body of the Streamlit cheat sheet with Pandas examples.
+ """
+ col1, col2, col3 = st.columns(3) # Create columns for layout
+
+ #######################################
+ # COLUMN 1
+ #######################################
+
+ # Read and Write to CSV
+ col1.subheader('Read and Write to CSV')
+ col1.code('''
+ # Read from CSV
+ df_read = pd.read_csv(
+ 'file.csv',
+ header=None,
+ nrows=5
+ )
+
+ # Write to CSV
+ df.to_csv('myDataFrame.csv')
+ ''')
+
+ # Read and Write to Excel
+ col1.subheader('Read and Write to Excel')
+ col1.code('''
+ # Read from Excel
+ df_read_excel = pd.read_excel('file.xlsx')
+
+ # Write to Excel
+ df.to_excel(
+ 'dir/myDataFrame.xlsx',
+ sheet_name='Sheet1'
+ )
+
+ # Read multiple sheets from the same file
+ xlsx = pd.ExcelFile('file.xls')
+ df_from_sheet1 = pd.read_excel(xlsx, 'Sheet1')
+ ''')
+
+ # Read and Write to SQL Query or Database Table
+ col1.subheader('Read and Write to SQL Query or Database Table')
+ col1.code('''
+ from sqlalchemy import create_engine
+ engine = create_engine('sqlite:///:memory:')
+
+ # Read from SQL Query
+ pd.read_sql("SELECT * FROM my_table;", engine)
+
+ # Read from Database Table
+ pd.read_sql_table('my_table', engine)
+
+ # Read from SQL Query using read_sql_query()
+ pd.read_sql_query("SELECT * FROM my_table;", engine)
+
+ # Write DataFrame to SQL Table
+ pd.to_sql('myDf', engine)
+ ''')
+
+
+ # Getting Elements from Series and DataFrame
+ col1.subheader('Getting Elements')
+ col1.code('''
+ # Get one element from a Series
+ s['b']
+ # Output: -5
+
+ # Get subset of a DataFrame
+ df[1:]
+ # Output:
+ # Country Capital Population
+ # 1 India New Delhi 1303171035
+ # 2 Brazil Brasília 207847528
+ ''')
+
+ # Asking For Help
+ col1.subheader('Asking For Help')
+ col1.code('''
+ # Display help for a function or object
+ help(pd.Series.loc)
+ ''')
+
+ #######################################
+ # COLUMN 2
+ #######################################
+
+ # Selecting, Boolean Indexing & Setting
+ col2.subheader('Selecting, Boolean Indexing & Setting')
+ col2.code('''
+ # Select single value by row & 'Belgium' column
+ df.iloc[[0],[0]]
+ # Output: 'Belgium'
+
+ # Select single value by row & 'Belgium' column labels
+ df.loc[[0], ['Country']]
+ # Output: 'Belgium'
+
+ # Select single row of subset of rows
+ df.loc[2]
+ # Output:
+ # Country Brazil
+ # Capital Brasília
+ # Population 207847528
+
+ # Select a single column of subset of columns
+ df.loc[:,'Capital']
+ # Output:
+ # 0 Brussels
+ # 1 New Delhi
+ # 2 Brasília
+
+ # Boolean indexing - Series s where value is not > 1
+ s[~(s > 1)]
+
+ # Boolean indexing - s where value is <-1 or >2
+ s[(s < -1) | (s > 2)]
+
+ # Use filter to adjust DataFrame
+ df[df['Population'] > 1200000000]
+
+ # Setting index a of Series s to 6
+ s['a'] = 6
+ ''')
+
+ # Dropping
+ col2.subheader('Dropping')
+ col2.code('''
+ # Drop values from rows (axis=0)
+ s.drop(['a', 'c'])
+
+ # Drop values from columns (axis=1)
+ df.drop('Country', axis=1)
+ ''')
+
+ # Sort & Rank
+ col2.subheader('Sort & Rank')
+ col2.code('''
+ # Sort by labels along an axis
+ df.sort_index()
+
+ # Sort by the values along an axis
+ df.sort_values(by='Country')
+
+ # Assign ranks to entries
+ df.rank()
+ ''')
+
+ # Applying Functions
+ col2.subheader('Applying Functions')
+ col2.code('''
+ # Define a function
+ f = lambda x: x*2
+
+ # Apply function to DataFrame
+ df.apply(f)
+
+ # Apply function element-wise
+ df.applymap(f)
+ ''')
+
+ #######################################
+ # COLUMN 3
+ #######################################
+
+ # Basic Information
+ col3.subheader('Basic Information')
+ col3.code('''
+ # Get the shape (rows, columns)
+ df.shape
+
+ # Describe index
+ df.index
+
+ # Describe DataFrame columns
+ df.columns
+
+ # Info on DataFrame
+ df.info()
+
+ # Number of non-NA values
+ df.count()
+ ''')
+
+ # Summary
+ col3.subheader('Summary')
+ col3.code('''
+ # Sum of values
+ df[col].sum()
+
+ # Cumulative sum of values
+ df[col].cumsum()
+
+ # Minimum/maximum values
+ df[col].min()
+ df[col].max()
+
+ # Index of minimum/maximum values
+ df[col].idxmin()
+ df[col].idxmax()
+
+ # Summary statistics
+ df[col].describe()
+
+ # Mean of values
+ df[col].mean()
+
+ # Median of values
+ df[col].median()
+ ''')
+
+ # Internal Data Alignment
+ col3.subheader('Internal Data Alignment')
+ col3.code('''
+ # Create Series with different indices
+ s3 = pd.Series([7, -2, 3], index=['a', 'c', 'd'])
+
+ # Add two Series with different indices
+ result = s + s3
+ ''')
+
+ # Arithmetic Operations with Fill Methods
+ col3.subheader('Arithmetic Operations with Fill Methods')
+ col3.code('''
+ # Perform arithmetic operations with fill methods
+ result_add = s.add(s3, fill_value=0)
+ result_sub = s.sub(s3, fill_value=2)
+ result_div = s.div(s3, fill_value=4)
+ result_mul = s.mul(s3, fill_value=3)
+ ''')
+
+# Run the main function if the script is executed directly
+if __name__ == '__main__':
+ main()
diff --git a/docs/examples/pandas/pandas.ipynb b/docs/examples/pandas/pandas.ipynb
new file mode 100644
index 0000000..05621c4
--- /dev/null
+++ b/docs/examples/pandas/pandas.ipynb
@@ -0,0 +1,1834 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "ae90d978",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "\n",
+ "# Pandas Cheat Sheet\n",
+ "\n",
+ "![\"numpy](\"https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/pandas/pandas.png\")
\n",
+ "\n",
+ "[Pandas](https://pandas.pydata.org/) is built on NumPy and provides easy-to-use\n",
+ "data structures and data analysis tools for the Python\n",
+ "programming language.\n",
+ "\n",
+ "## Install and import Pandas\n",
+ "\n",
+ "`\n",
+ "$ pip install pandas\n",
+ "`\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "805a07df",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import Pandas convention\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "75775834",
+ "metadata": {},
+ "source": [
+ "## Pandas Data Structures\n",
+ "\n",
+ "**Series**\n",
+ "\n",
+ "![\"numpy](\"https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/pandas/serie.png\")
\n",
+ "\n",
+ "A **one-dimensional** labeled array a capable of holding any data type."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "fbef430f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "s:\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "a 3\n",
+ "b -5\n",
+ "c 7\n",
+ "d 4\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Create a pandas Series\n",
+ "s = pd.Series(\n",
+ " [3, -5, 7, 4],\n",
+ " index=['a', 'b', 'c', 'd']\n",
+ ")\n",
+ "\n",
+ "# Print the pandas Series\n",
+ "print(\"s:\")\n",
+ "s"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "feba27ca",
+ "metadata": {},
+ "source": [
+ "**DataFrame**\n",
+ "\n",
+ "![\"numpy](\"https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/pandas/df.png\")
\n",
+ "\n",
+ "**two-dimensional** labeled data structure with columns of potentially different types."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "7bcbaad2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "df:\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | Country | \n", + "Capital | \n", + "Population | \n", + "
|---|---|---|---|
| 0 | \n", + "Belgium | \n", + "Brussels | \n", + "11190846 | \n", + "
| 1 | \n", + "India | \n", + "New Delhi | \n", + "1303171035 | \n", + "
| 2 | \n", + "Brazil | \n", + "Brasília | \n", + "207847528 | \n", + "
| \n", + " | Country | \n", + "Capital | \n", + "Population | \n", + "
|---|---|---|---|
| 1 | \n", + "India | \n", + "New Delhi | \n", + "1303171035 | \n", + "
| 2 | \n", + "Brazil | \n", + "Brasília | \n", + "207847528 | \n", + "
| \n", + " | Country | \n", + "
|---|---|
| 0 | \n", + "Belgium | \n", + "
| \n", + " | Country | \n", + "
|---|---|
| 0 | \n", + "Belgium | \n", + "
| \n", + " | Country | \n", + "Capital | \n", + "Population | \n", + "
|---|---|---|---|
| 1 | \n", + "India | \n", + "New Delhi | \n", + "1303171035 | \n", + "
| \n", + " | Capital | \n", + "Population | \n", + "
|---|---|---|
| 0 | \n", + "Brussels | \n", + "11190846 | \n", + "
| 1 | \n", + "New Delhi | \n", + "1303171035 | \n", + "
| 2 | \n", + "Brasília | \n", + "207847528 | \n", + "
| \n", + " | Country | \n", + "Capital | \n", + "Population | \n", + "
|---|---|---|---|
| 0 | \n", + "Belgium | \n", + "Brussels | \n", + "11190846 | \n", + "
| 1 | \n", + "India | \n", + "New Delhi | \n", + "1303171035 | \n", + "
| 2 | \n", + "Brazil | \n", + "Brasília | \n", + "207847528 | \n", + "
| \n", + " | Country | \n", + "Capital | \n", + "Population | \n", + "
|---|---|---|---|
| 0 | \n", + "Belgium | \n", + "Brussels | \n", + "11190846 | \n", + "
| 2 | \n", + "Brazil | \n", + "Brasília | \n", + "207847528 | \n", + "
| 1 | \n", + "India | \n", + "New Delhi | \n", + "1303171035 | \n", + "
| \n", + " | Country | \n", + "Capital | \n", + "Population | \n", + "
|---|---|---|---|
| 0 | \n", + "1.0 | \n", + "2.0 | \n", + "1.0 | \n", + "
| 1 | \n", + "3.0 | \n", + "3.0 | \n", + "3.0 | \n", + "
| 2 | \n", + "2.0 | \n", + "1.0 | \n", + "2.0 | \n", + "
| \n", + " | Country | \n", + "Capital | \n", + "Population | \n", + "
|---|---|---|---|
| 0 | \n", + "BelgiumBelgium | \n", + "BrusselsBrussels | \n", + "22381692 | \n", + "
| 1 | \n", + "IndiaIndia | \n", + "New DelhiNew Delhi | \n", + "2606342070 | \n", + "
| 2 | \n", + "BrazilBrazil | \n", + "BrasíliaBrasília | \n", + "415695056 | \n", + "
| \n", + " | Country | \n", + "Capital | \n", + "Population | \n", + "
|---|---|---|---|
| 0 | \n", + "BelgiumBelgium | \n", + "BrusselsBrussels | \n", + "22381692 | \n", + "
| 1 | \n", + "IndiaIndia | \n", + "New DelhiNew Delhi | \n", + "2606342070 | \n", + "
| 2 | \n", + "BrazilBrazil | \n", + "BrasíliaBrasília | \n", + "415695056 | \n", + "
\n",
+ "\n",
+ " \n",
+ "# Pandas Cheat Sheet"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7570e16a",
+ "metadata": {},
+ "source": [
+ "\n",
+ " ](https://streamlit.io/)'''.format(\n",
+ " img_to_bytes(\"https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/pandas/pandas.png\")), unsafe_allow_html=True)\n",
+ "\n",
+ " st.sidebar.header('Pandas Cheat Sheet')\n",
+ " st.sidebar.markdown('''\n",
+ "[Pandas](https://pandas.pydata.org/) is built on NumPy and provides easy-to-use\n",
+ "data structures and data analysis tools for the Python\n",
+ "programming language.\n",
+ " ''', unsafe_allow_html=True)\n",
+ "\n",
+ " # Pandas installation and import\n",
+ " st.sidebar.markdown('__Install and import Pandas__')\n",
+ " st.sidebar.code('$ pip install pandas')\n",
+ " st.sidebar.code('''\n",
+ "# Import Pandas convention\n",
+ ">>> import pandas as pd\n",
+ "''')\n",
+ "\n",
+ " # Pandas array creation\n",
+ " st.sidebar.subheader('Pandas Data Structures')\n",
+ " st.sidebar.markdown('__Series__')\n",
+ " st.sidebar.markdown(\n",
+ " '''[](https://streamlit.io/)'''.format(\n",
+ " img_to_bytes(\"https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/pandas/serie.png\")), unsafe_allow_html=True)\n",
+ "\n",
+ " st.sidebar.markdown('''\n",
+ " A **one-dimensional** labeled array a capable of holding any data type.\n",
+ " ''', unsafe_allow_html=True)\n",
+ "\n",
+ " st.sidebar.code('''\n",
+ " # Create a pandas Series\n",
+ " s = pd.Series(\n",
+ " [3, -5, 7, 4],\n",
+ " index=['a', 'b', 'c', 'd']\n",
+ " )\n",
+ " ''')\n",
+ "\n",
+ " st.sidebar.markdown('__DataFrame__')\n",
+ " st.sidebar.markdown('''\n",
+ " A **two-dimensional** labeled data structure with columns of potentially different types.\n",
+ " ''', unsafe_allow_html=True)\n",
+ " st.sidebar.markdown(\n",
+ " '''[](https://streamlit.io/)'''.format(\n",
+ " img_to_bytes(\"https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/pandas/df.png\")), unsafe_allow_html=True)\n",
+ "\n",
+ " st.sidebar.code('''\n",
+ " # Create a pandas DataFrame\n",
+ " data = {\n",
+ " 'Country': ['Belgium', 'India', 'Brazil'],\n",
+ " 'Capital': ['Brussels', 'New Delhi', 'Brasília'],\n",
+ " 'Population': [11190846, 1303171035, 207847528]\n",
+ " }\n",
+ " df = pd.DataFrame(\n",
+ " data,\n",
+ " columns=['Country', 'Capital', 'Population']\n",
+ " )\n",
+ " ''')\n",
+ "\n",
+ " return None\n",
+ "\n",
+ "\n",
+ "# Define the cs_body() function\n",
+ "def cs_body():\n",
+ " \"\"\"\n",
+ " Create content sections for the main body of the Streamlit cheat sheet with Pandas examples.\n",
+ " \"\"\"\n",
+ " col1, col2, col3 = st.columns(3) # Create columns for layout\n",
+ "\n",
+ " #######################################\n",
+ " # COLUMN 1\n",
+ " #######################################\n",
+ "\n",
+ " # Read and Write to CSV\n",
+ " col1.subheader('Read and Write to CSV')\n",
+ " col1.code('''\n",
+ " # Read from CSV\n",
+ " df_read = pd.read_csv(\n",
+ " 'file.csv',\n",
+ " header=None, \n",
+ " nrows=5\n",
+ " )\n",
+ "\n",
+ " # Write to CSV\n",
+ " df.to_csv('myDataFrame.csv')\n",
+ " ''')\n",
+ "\n",
+ " # Read and Write to Excel\n",
+ " col1.subheader('Read and Write to Excel')\n",
+ " col1.code('''\n",
+ " # Read from Excel\n",
+ " df_read_excel = pd.read_excel('file.xlsx')\n",
+ "\n",
+ " # Write to Excel\n",
+ " df.to_excel(\n",
+ " 'dir/myDataFrame.xlsx', \n",
+ " sheet_name='Sheet1'\n",
+ " )\n",
+ "\n",
+ " # Read multiple sheets from the same file\n",
+ " xlsx = pd.ExcelFile('file.xls')\n",
+ " df_from_sheet1 = pd.read_excel(xlsx, 'Sheet1')\n",
+ " ''')\n",
+ "\n",
+ " # Read and Write to SQL Query or Database Table\n",
+ " col1.subheader('Read and Write to SQL Query or Database Table')\n",
+ " col1.code('''\n",
+ " from sqlalchemy import create_engine\n",
+ " engine = create_engine('sqlite:///:memory:')\n",
+ "\n",
+ " # Read from SQL Query\n",
+ " pd.read_sql(\"SELECT * FROM my_table;\", engine)\n",
+ "\n",
+ " # Read from Database Table\n",
+ " pd.read_sql_table('my_table', engine)\n",
+ "\n",
+ " # Read from SQL Query using read_sql_query()\n",
+ " pd.read_sql_query(\"SELECT * FROM my_table;\", engine)\n",
+ "\n",
+ " # Write DataFrame to SQL Table\n",
+ " pd.to_sql('myDf', engine)\n",
+ " ''')\n",
+ "\n",
+ "\n",
+ " # Getting Elements from Series and DataFrame\n",
+ " col1.subheader('Getting Elements')\n",
+ " col1.code('''\n",
+ " # Get one element from a Series\n",
+ " s['b']\n",
+ " # Output: -5\n",
+ "\n",
+ " # Get subset of a DataFrame\n",
+ " df[1:]\n",
+ " # Output:\n",
+ " # Country Capital Population\n",
+ " # 1 India New Delhi 1303171035\n",
+ " # 2 Brazil Brasília 207847528\n",
+ " ''')\n",
+ "\n",
+ " # Asking For Help\n",
+ " col1.subheader('Asking For Help')\n",
+ " col1.code('''\n",
+ " # Display help for a function or object\n",
+ " help(pd.Series.loc)\n",
+ " ''')\n",
+ "\n",
+ " #######################################\n",
+ " # COLUMN 2\n",
+ " #######################################\n",
+ "\n",
+ " # Selecting, Boolean Indexing & Setting\n",
+ " col2.subheader('Selecting, Boolean Indexing & Setting')\n",
+ " col2.code('''\n",
+ " # Select single value by row & 'Belgium' column\n",
+ " df.iloc[[0],[0]]\n",
+ " # Output: 'Belgium'\n",
+ "\n",
+ " # Select single value by row & 'Belgium' column labels\n",
+ " df.loc[[0], ['Country']]\n",
+ " # Output: 'Belgium'\n",
+ "\n",
+ " # Select single row of subset of rows\n",
+ " df.loc[2]\n",
+ " # Output:\n",
+ " # Country Brazil\n",
+ " # Capital Brasília\n",
+ " # Population 207847528\n",
+ "\n",
+ " # Select a single column of subset of columns\n",
+ " df.loc[:,'Capital']\n",
+ " # Output:\n",
+ " # 0 Brussels\n",
+ " # 1 New Delhi\n",
+ " # 2 Brasília\n",
+ "\n",
+ " # Boolean indexing - Series s where value is not > 1\n",
+ " s[~(s > 1)]\n",
+ "\n",
+ " # Boolean indexing - s where value is <-1 or >2\n",
+ " s[(s < -1) | (s > 2)]\n",
+ "\n",
+ " # Use filter to adjust DataFrame\n",
+ " df[df['Population'] > 1200000000]\n",
+ "\n",
+ " # Setting index a of Series s to 6\n",
+ " s['a'] = 6\n",
+ " ''')\n",
+ "\n",
+ " # Dropping\n",
+ " col2.subheader('Dropping')\n",
+ " col2.code('''\n",
+ " # Drop values from rows (axis=0)\n",
+ " s.drop(['a', 'c'])\n",
+ "\n",
+ " # Drop values from columns (axis=1)\n",
+ " df.drop('Country', axis=1)\n",
+ " ''')\n",
+ "\n",
+ " # Sort & Rank\n",
+ " col2.subheader('Sort & Rank')\n",
+ " col2.code('''\n",
+ " # Sort by labels along an axis\n",
+ " df.sort_index()\n",
+ "\n",
+ " # Sort by the values along an axis\n",
+ " df.sort_values(by='Country')\n",
+ "\n",
+ " # Assign ranks to entries\n",
+ " df.rank()\n",
+ " ''')\n",
+ "\n",
+ " # Applying Functions\n",
+ " col2.subheader('Applying Functions')\n",
+ " col2.code('''\n",
+ " # Define a function\n",
+ " f = lambda x: x*2\n",
+ "\n",
+ " # Apply function to DataFrame\n",
+ " df.apply(f)\n",
+ "\n",
+ " # Apply function element-wise\n",
+ " df.applymap(f)\n",
+ " ''')\n",
+ "\n",
+ " #######################################\n",
+ " # COLUMN 3\n",
+ " #######################################\n",
+ "\n",
+ " # Basic Information\n",
+ " col3.subheader('Basic Information')\n",
+ " col3.code('''\n",
+ " # Get the shape (rows, columns)\n",
+ " df.shape\n",
+ "\n",
+ " # Describe index\n",
+ " df.index\n",
+ "\n",
+ " # Describe DataFrame columns\n",
+ " df.columns\n",
+ "\n",
+ " # Info on DataFrame\n",
+ " df.info()\n",
+ "\n",
+ " # Number of non-NA values\n",
+ " df.count()\n",
+ " ''')\n",
+ "\n",
+ " # Summary\n",
+ " col3.subheader('Summary')\n",
+ " col3.code('''\n",
+ " # Sum of values\n",
+ " df[col].sum()\n",
+ "\n",
+ " # Cumulative sum of values\n",
+ " df[col].cumsum()\n",
+ "\n",
+ " # Minimum/maximum values\n",
+ " df[col].min()\n",
+ " df[col].max()\n",
+ "\n",
+ " # Index of minimum/maximum values\n",
+ " df[col].idxmin()\n",
+ " df[col].idxmax()\n",
+ "\n",
+ " # Summary statistics\n",
+ " df[col].describe()\n",
+ "\n",
+ " # Mean of values\n",
+ " df[col].mean()\n",
+ "\n",
+ " # Median of values\n",
+ " df[col].median()\n",
+ " ''')\n",
+ "\n",
+ " # Internal Data Alignment\n",
+ " col3.subheader('Internal Data Alignment')\n",
+ " col3.code('''\n",
+ " # Create Series with different indices\n",
+ " s3 = pd.Series([7, -2, 3], index=['a', 'c', 'd'])\n",
+ "\n",
+ " # Add two Series with different indices\n",
+ " result = s + s3\n",
+ " ''')\n",
+ "\n",
+ " # Arithmetic Operations with Fill Methods\n",
+ " col3.subheader('Arithmetic Operations with Fill Methods')\n",
+ " col3.code('''\n",
+ " # Perform arithmetic operations with fill methods\n",
+ " result_add = s.add(s3, fill_value=0)\n",
+ " result_sub = s.sub(s3, fill_value=2)\n",
+ " result_div = s.div(s3, fill_value=4)\n",
+ " result_mul = s.mul(s3, fill_value=3)\n",
+ " ''')\n",
+ "\n",
+ "# Run the main function if the script is executed directly\n",
+ "if __name__ == '__main__':\n",
+ " main()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b50b0160",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!streamlit run app.py & npx localtunnel --port 8501"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/python/app.py b/docs/examples/python/app.py
new file mode 100644
index 0000000..85056e6
--- /dev/null
+++ b/docs/examples/python/app.py
@@ -0,0 +1,325 @@
+import streamlit as st
+from pathlib import Path
+import base64
+import requests
+
+
+# Initial page config
+st.set_page_config(
+ page_title='Python Cheat Sheet',
+ layout="wide",
+ initial_sidebar_state="expanded",
+)
+
+def main():
+ """
+ Main function to set up the Streamlit app layout.
+ """
+ cs_sidebar()
+ cs_body()
+ return None
+
+# Define img_to_bytes() function
+def img_to_bytes(img_url):
+ response = requests.get(img_url)
+ img_bytes = response.content
+ encoded = base64.b64encode(img_bytes).decode()
+ return encoded
+
+# Define the cs_sidebar() function
+def cs_sidebar():
+ """
+ Populate the sidebar with various content sections related to Python.
+ """
+ st.sidebar.markdown(
+ '''[](https://streamlit.io/)'''.format(
+ img_to_bytes("https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/python/python.png")), unsafe_allow_html=True)
+
+ st.sidebar.header('Python Cheat Sheet')
+ st.sidebar.markdown('''
+[Python](https://www.python.org/) is a high-level, interpreted programming
+ language known for its simplicity, readability, and versatility.
+ Created by Guido van Rossum and first released in 1991,
+ Python has gained immense popularity in various domains,
+ including web development, data science, automation, and more.
+ ''', unsafe_allow_html=True)
+
+ # why python ?
+ st.sidebar.markdown('__Why Python?__')
+ st.sidebar.markdown('''
+ Python has experienced a remarkable surge in popularity over the years and has become one of the most
+ widely used programming languages across various fields. ''', unsafe_allow_html=True)
+
+ st.sidebar.markdown(
+ '''[](https://streamlit.io/)'''.format(
+ img_to_bytes("https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/python/survey2.png")), unsafe_allow_html=True)
+
+ return None
+
+
+# Define the cs_body() function
+def cs_body():
+ """
+ Create content sections for the main body of the Streamlit cheat sheet with Python examples.
+ """
+ col1, col2, col3 = st.columns(3) # Create columns for layout
+
+ #######################################
+ # COLUMN 1
+ #######################################
+
+ # Hello, World!
+ col1.subheader('Hello, World!')
+ col1.code('''
+ print("Hello, World!")
+ ''')
+
+ # Comments
+ col1.subheader('Comments')
+ col1.code('''
+ # This is a comment
+ ''')
+
+ # Variables and Data Types
+ col1.subheader('Variables and Data Types')
+ col1.code('''
+ x = 5 # Integer
+ y = 3.14 # Float
+ name = "John" # String
+ is_student = True # Boolean
+ ''')
+
+ # Basic Operations
+ col1.subheader('Basic Operations')
+ col1.code('''
+ # Perform basic arithmetic operations
+ sum_result = x + y
+ sub_result = x - y
+ mul_result = x * y
+ div_result = x / y
+ ''')
+
+
+ # String Operations
+ col1.subheader('String Operations')
+ col1.code('''
+ # String concatenation
+ full_name = name + " Doe"
+
+ # Formatted string
+ formatted_string = f"Hello, {name}!"
+ ''')
+
+ # Input and Output
+ col1.subheader('Input and Output')
+ col1.code('''
+ # Get user input and display it
+ user_input = input("Enter a number: ")
+ print("You entered:", user_input)
+ ''')
+
+ # File Handling
+ col1.subheader('File Handling')
+ col1.code('''
+ # Read content from a file
+ with open("file.txt", "r") as file:
+ content = file.read()
+
+ # Write content to a new file
+ with open("new_file.txt", "w") as new_file:
+ new_file.write("Hello, world!")
+ ''')
+
+ # Conditional Statements
+ col1.subheader('Conditional Statements')
+ col1.code('''
+ # Check if x is greater than y
+ if x > y:
+ print("x is greater")
+ # If not, check if x is less than y
+ elif x < y:
+ print("y is greater")
+ # If neither condition is true, they must be equal
+ else:
+ print("x and y are equal")
+ ''')
+
+ #######################################
+ # COLUMN 2
+ #######################################
+
+ # Creating lists
+ col2.subheader('Creating Lists')
+ col2.code('''
+ # Create lists with [], elements separated by commas
+ x = [1, 3, 2]
+ ''')
+
+ # List functions and methods
+ col2.subheader('List Functions and Methods')
+ col2.code('''
+ # Return a sorted copy of the list e.g., [1,2,3]
+ sorted_list = sorted(x)
+
+ # Sorts the list in place (replaces x)
+ x.sort()
+
+ # Reverse the order of elements in x e.g., [2,3,1]
+ reversed_list = list(reversed(x))
+
+ # Reverse the list in place
+ x.reverse()
+
+ # Count the number of element 2 in the list
+ count_2 = x.count(2)
+ ''')
+
+ # Selecting list elements
+ col2.subheader('Selecting List Elements')
+ col2.code('''
+ # Select the 0th element in the list
+ element_0 = x[0]
+
+ # Select the last element in the list
+ last_element = x[-1]
+
+ # Select 1st (inclusive) to 3rd (exclusive)
+ subset_1_to_3 = x[1:3]
+
+ # Select the 2nd to the end
+ subset_2_to_end = x[2:]
+
+ # Select 0th to 3rd (exclusive)
+ subset_0_to_3 = x[:3]
+ ''')
+
+ # Concatenating lists
+ col2.subheader('Concatenating Lists')
+ col2.code('''
+ # Define the x and y lists
+ x = [1, 3, 6]
+ y = [10, 15, 21]
+
+ # Concatenate lists using '+'
+ concatenated_list = x + y
+
+ # Replicate elements in a list using '*'
+ replicated_list = 3 * x
+ ''')
+
+ # Creating dictionaries
+ col2.subheader('Creating Dictionaries')
+ col2.code('''
+ # Create a dictionary with {}
+ my_dict = {'a': 1, 'b': 2, 'c': 3}
+
+ ''')
+
+ # Dictionary functions and methods
+ col2.subheader('Dictionary Functions and Methods')
+ col2.code('''
+ # Get the keys of a dictionary
+ my_dict.keys() # Returns dict_keys(['a', 'b', 'c'])
+
+ # Get the values of a dictionary
+ my_dict.values() # Returns dict_values([1, 2, 3])
+
+ # Get a value from a dictionary by specifying the key
+ my_dict['a'] # Returns 1
+ ''')
+
+ #######################################
+ # COLUMN 3
+ #######################################
+
+ # Loops
+ col3.subheader('Loops')
+ col3.code('''
+ # For loop
+ numbers = [1, 2, 3, 4, 5]
+ for num in numbers:
+ print(num)
+
+ # While loop
+ x = 5
+ while x > 0:
+ print(x)
+ x -= 1
+ ''')
+
+ # Error Handling
+ col3.subheader('Error Handling')
+ col3.code('''
+ # Try to perform division
+ try:
+ result = x / y # Attempt to divide x by y
+ except ZeroDivisionError as e: # If a ZeroDivisionError occurs
+ print("Cannot divide by zero")
+ print(e)
+ ''')
+
+ # List Comprehensions
+ col3.subheader('List Comprehensions')
+ col3.code('''
+ # Create a list of squared numbers using a list comprehension
+ squared_numbers = [num**2 for num in numbers]
+
+ # Create a list of even numbers using a list comprehension with condition
+ even_numbers = [num for num in numbers if num % 2 == 0]
+ ''')
+
+ # Functions
+ col3.subheader('Functions')
+ col3.code('''
+ # Define a function that takes a name parameter
+ def greet(name):
+ return f"Hello, {name}!"
+
+ # Call the greet function with the argument "Alice"
+ greeting = greet("Alice")
+ ''')
+
+ # Built-in Functions
+ col3.subheader('Built-in Functions')
+ col3.code('''
+ # Get the length of a list
+ len_fruits = len(fruits)
+
+ # Find the maximum value in a list
+ max_number = max(numbers)
+
+ # Find the minimum value in a list
+ min_number = min(numbers)
+ ''')
+
+ # Importing Modules
+ col3.subheader('Importing Modules')
+ col3.code('''
+ import math
+ sqrt_result = math.sqrt(x) # Calculate square root using math module
+
+ from random import randint
+ random_number = randint(1, 10) # Generate a random number between 1 and 10
+
+ import math
+ sqrt_result = math.sqrt(x) # Reusing the math module for another calculation
+ ''')
+
+ # Classes and Objects
+ col3.subheader('Classes and Objects')
+ col3.code('''
+ class Dog:
+ def __init__(self, name, age):
+ self.name = name
+ self.age = age
+
+ def bark(self):
+ return "Woof!"
+
+ my_dog = Dog("Buddy", 3)
+ ''')
+
+
+# Run the main function if the script is executed directly
+if __name__ == '__main__':
+ main()
diff --git a/docs/examples/python/python.ipynb b/docs/examples/python/python.ipynb
new file mode 100644
index 0000000..ebad7bc
--- /dev/null
+++ b/docs/examples/python/python.ipynb
@@ -0,0 +1,769 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0f757565",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ " \n",
+ "# Python Cheat Sheet\n",
+ "\n",
+ "![\"numpy](\"https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/python/python.png\")
\n",
+ "\n",
+ "[Python](https://www.python.org/) is a high-level, interpreted programming\n",
+ " language known for its simplicity, readability, and versatility. \n",
+ " Created by Guido van Rossum and first released in 1991,\n",
+ " Python has gained immense popularity in various domains,\n",
+ " including web development, data science, automation, and more."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bbc8b478",
+ "metadata": {},
+ "source": [
+ "## Why Python?\n",
+ "\n",
+ "Python has experienced a remarkable surge in popularity over the years and has become one of the most \n",
+ " widely used programming languages across various fields. \n",
+ "\n",
+ "![\"numpy](\"https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/python/survey2.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8e2f9b13",
+ "metadata": {},
+ "source": [
+ "## Hello, World!\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "3002d601",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Hello, World!\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"Hello, World!\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2f88a434",
+ "metadata": {},
+ "source": [
+ "## Comments"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "cbe1bb8e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# This is a comment"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7e310817",
+ "metadata": {},
+ "source": [
+ "## Variables and Data Types\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "350dfa62",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "x: 5\n",
+ "y: 3.14\n",
+ "name: John\n",
+ "is_student: True\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Define an integer variable\n",
+ "x = 5 # Integer\n",
+ "\n",
+ "# Define a float variable\n",
+ "y = 3.14 # Float\n",
+ "\n",
+ "# Define a string variable\n",
+ "name = \"John\" # String\n",
+ "\n",
+ "# Define a boolean variable\n",
+ "is_student = True # Boolean\n",
+ "\n",
+ "# Print the values of the variables\n",
+ "print(\"x:\", x)\n",
+ "print(\"y:\", y)\n",
+ "print(\"name:\", name)\n",
+ "print(\"is_student:\", is_student)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bd0ecf8a",
+ "metadata": {},
+ "source": [
+ "## Basic Operations\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "220590d0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "sum_result: 8.14\n",
+ "sub_result: 1.8599999999999999\n",
+ "mul_result: 15.700000000000001\n",
+ "div_result: 1.592356687898089\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Perform basic arithmetic operations\n",
+ "sum_result = x + y # Add x and y\n",
+ "sub_result = x - y # Subtract y from x\n",
+ "mul_result = x * y # Multiply x and y\n",
+ "div_result = x / y # Divide x by y\n",
+ "\n",
+ "# Print the results of the arithmetic operations\n",
+ "print(\"sum_result:\", sum_result)\n",
+ "print(\"sub_result:\", sub_result)\n",
+ "print(\"mul_result:\", mul_result)\n",
+ "print(\"div_result:\", div_result)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bf401d58",
+ "metadata": {},
+ "source": [
+ "## String Operations\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "be3e2926",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "full_name: John Doe\n",
+ "formatted_string: Hello, John!\n"
+ ]
+ }
+ ],
+ "source": [
+ "# String concatenation\n",
+ "full_name = name + \" Doe\" # Concatenate 'name' and \" Doe\"\n",
+ "\n",
+ "# Formatted string\n",
+ "formatted_string = f\"Hello, {name}!\" # Create a formatted string using 'name'\n",
+ "\n",
+ "# Print the results of string operations\n",
+ "print(\"full_name:\", full_name)\n",
+ "print(\"formatted_string:\", formatted_string)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b167a75f",
+ "metadata": {},
+ "source": [
+ "## Conditional Statements\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "ffee9551",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "x is greater\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Define the values of x and y\n",
+ "x = 7\n",
+ "y = 3.5\n",
+ "\n",
+ "# Check if x is greater than y\n",
+ "if x > y:\n",
+ " print(\"x is greater\")\n",
+ "# If not, check if x is less than y\n",
+ "elif x < y:\n",
+ " print(\"y is greater\")\n",
+ "# If neither condition is true, they must be equal\n",
+ "else:\n",
+ " print(\"x and y are equal\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "be074577",
+ "metadata": {},
+ "source": [
+ "## Lists"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a3e8ead9",
+ "metadata": {},
+ "source": [
+ "### Creating Lists\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "360e7e48",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "x: [1, 3, 2]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Create a list with elements 1, 3, and 2\n",
+ "x = [1, 3, 2]\n",
+ "\n",
+ "# Print the list\n",
+ "print(\"x:\", x)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "66171d3a",
+ "metadata": {},
+ "source": [
+ "### List Functions and Methods\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "300fcfec",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "sorted_list: [1, 2, 3]\n",
+ "reversed_list: [3, 2, 1]\n",
+ "count_2: 1\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Return a sorted copy of the list\n",
+ "sorted_list = sorted(x) # Creates a new list with elements in sorted order\n",
+ "\n",
+ "# Sorts the list in place (replaces x)\n",
+ "x.sort() # Modifies the existing list x to be sorted\n",
+ "\n",
+ "# Reverse the order of elements in x\n",
+ "reversed_list = list(reversed(x)) # Creates a new list with elements in reversed order\n",
+ "\n",
+ "# Reverse the list in place\n",
+ "x.reverse() # Modifies the existing list x to be reversed\n",
+ "\n",
+ "# Count the number of element 2 in the list\n",
+ "count_2 = x.count(2) # Counts the occurrences of element 2 in the list x\n",
+ "\n",
+ "# Print the results of list operations\n",
+ "print(\"sorted_list:\", sorted_list)\n",
+ "print(\"reversed_list:\", reversed_list)\n",
+ "print(\"count_2:\", count_2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "032c5e43",
+ "metadata": {},
+ "source": [
+ "### Selecting List Elements\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "69b325d7",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "element_0: 3\n",
+ "last_element: 1\n",
+ "subset_1_to_3: [2, 1]\n",
+ "subset_2_to_end: [1]\n",
+ "subset_0_to_3: [3, 2, 1]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Select the 0th element in the list\n",
+ "element_0 = x[0] # Assigns the first element of x to element_0\n",
+ "\n",
+ "# Select the last element in the list\n",
+ "last_element = x[-1] # Assigns the last element of x to last_element\n",
+ "\n",
+ "# Select 1st (inclusive) to 3rd (exclusive)\n",
+ "subset_1_to_3 = x[1:3] # Creates a subset containing elements from index 1 to 2\n",
+ "\n",
+ "# Select the 2nd to the end\n",
+ "subset_2_to_end = x[2:] # Creates a subset containing elements from index 2 to the end\n",
+ "\n",
+ "# Select 0th to 3rd (exclusive)\n",
+ "subset_0_to_3 = x[:3] # Creates a subset containing elements from index 0 to 2\n",
+ "\n",
+ "# Print the selected elements and subsets\n",
+ "print(\"element_0:\", element_0)\n",
+ "print(\"last_element:\", last_element)\n",
+ "print(\"subset_1_to_3:\", subset_1_to_3)\n",
+ "print(\"subset_2_to_end:\", subset_2_to_end)\n",
+ "print(\"subset_0_to_3:\", subset_0_to_3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5644fffd",
+ "metadata": {},
+ "source": [
+ "### Concatenating Lists\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "5e80b6fa",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "concatenated_list: [1, 3, 6, 10, 15, 21]\n",
+ "replicated_list: [1, 3, 6, 1, 3, 6, 1, 3, 6]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Define the x and y lists\n",
+ "x = [1, 3, 6]\n",
+ "y = [10, 15, 21]\n",
+ "\n",
+ "# Concatenate lists using '+'\n",
+ "concatenated_list = x + y # Creates a new list by concatenating x and y\n",
+ "\n",
+ "# Replicate elements in a list using '*'\n",
+ "replicated_list = 3 * x # Creates a new list with elements of x replicated 3 times\n",
+ "\n",
+ "# Print the results of list operations\n",
+ "print(\"concatenated_list:\", concatenated_list)\n",
+ "print(\"replicated_list:\", replicated_list)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00b306cc",
+ "metadata": {},
+ "source": [
+ "## Dictionaries\n",
+ "\n",
+ "### Creating Dictionaries\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "54cbfcd7",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "my_dict: {'a': 1, 'b': 2, 'c': 3}\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Create a dictionary with key-value pairs\n",
+ "my_dict = {'a': 1, 'b': 2, 'c': 3}\n",
+ "\n",
+ "# Print the dictionary\n",
+ "print(\"my_dict:\", my_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "95c135cd",
+ "metadata": {},
+ "source": [
+ "### Dictionary Functions and Methods\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "692806a1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "keys: dict_keys(['a', 'b', 'c'])\n",
+ "values: dict_values([1, 2, 3])\n",
+ "value_a: 1\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Get the keys of a dictionary\n",
+ "keys = my_dict.keys() # Returns dict_keys(['a', 'b', 'c'])\n",
+ "\n",
+ "# Get the values of a dictionary\n",
+ "values = my_dict.values() # Returns dict_values([1, 2, 3])\n",
+ "\n",
+ "# Get a value from a dictionary by specifying the key\n",
+ "value_a = my_dict['a'] # Returns 1\n",
+ "\n",
+ "# Print the results of dictionary operations\n",
+ "print(\"keys:\", keys)\n",
+ "print(\"values:\", values)\n",
+ "print(\"value_a:\", value_a)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "459a3a1a",
+ "metadata": {},
+ "source": [
+ "## Loops"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "2353c0d8",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Using a for loop:\n",
+ "1\n",
+ "2\n",
+ "3\n",
+ "4\n",
+ "5\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Define a list of numbers\n",
+ "numbers = [1, 2, 3, 4, 5]\n",
+ "\n",
+ "# For loop\n",
+ "print(\"Using a for loop:\")\n",
+ "for num in numbers:\n",
+ " print(num)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "7f2027a8",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Using a while loop:\n",
+ "5\n",
+ "4\n",
+ "3\n",
+ "2\n",
+ "1\n"
+ ]
+ }
+ ],
+ "source": [
+ "# While loop\n",
+ "x = 5\n",
+ "print(\"Using a while loop:\")\n",
+ "while x > 0:\n",
+ " print(x)\n",
+ " x -= 1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d5022fc8",
+ "metadata": {},
+ "source": [
+ "## Functions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "23450976",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "greeting: Hello, Alice!\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Define a function that takes a name parameter\n",
+ "def greet(name):\n",
+ " return f\"Hello, {name}!\"\n",
+ "\n",
+ "# Call the greet function with the argument \"Alice\"\n",
+ "greeting = greet(\"Alice\") # Calls the greet function and stores the result in greeting\n",
+ "\n",
+ "# Print the greeting\n",
+ "print(\"greeting:\", greeting)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1804574a",
+ "metadata": {},
+ "source": [
+ "## Built-in Functions\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "acc5fc30",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Length of fruits list: 4\n",
+ "Maximum value in numbers list: 42\n",
+ "Minimum value in numbers list: 5\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Define a list of fruits and numbers\n",
+ "fruits = ['apple', 'banana', 'orange', 'kiwi']\n",
+ "numbers = [14, 27, 8, 42, 5]\n",
+ "\n",
+ "# Get the length of the list 'fruits'\n",
+ "len_fruits = len(fruits)\n",
+ "\n",
+ "# Find the maximum value in the list 'numbers'\n",
+ "max_number = max(numbers)\n",
+ "\n",
+ "# Find the minimum value in the list 'numbers'\n",
+ "min_number = min(numbers)\n",
+ "\n",
+ "# Print the results\n",
+ "print(\"Length of fruits list:\", len_fruits)\n",
+ "print(\"Maximum value in numbers list:\", max_number)\n",
+ "print(\"Minimum value in numbers list:\", min_number)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8bf0e51",
+ "metadata": {},
+ "source": [
+ "## Importing Modules\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "769c0a3a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "sqrt_result: 0.0\n",
+ "random_number: 6\n",
+ "sqrt_result_reuse: 0.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "import math\n",
+ "\n",
+ "# Calculate square root using math module\n",
+ "sqrt_result = math.sqrt(x)\n",
+ "\n",
+ "# Generate a random number between 1 and 10\n",
+ "from random import randint\n",
+ "random_number = randint(1, 10)\n",
+ "\n",
+ "# Reusing the math module for another calculation\n",
+ "sqrt_result_reuse = math.sqrt(x)\n",
+ "\n",
+ "# Print the results\n",
+ "print(\"sqrt_result:\", sqrt_result)\n",
+ "print(\"random_number:\", random_number)\n",
+ "print(\"sqrt_result_reuse:\", sqrt_result_reuse)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "096c64c2",
+ "metadata": {},
+ "source": [
+ "## Classes and Objects\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "82b596e2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "my_dog name: Buddy\n",
+ "my_dog age: 3\n",
+ "bark_result: Woof!\n"
+ ]
+ }
+ ],
+ "source": [
+ "class Dog:\n",
+ " def __init__(self, name, age):\n",
+ " self.name = name\n",
+ " self.age = age\n",
+ "\n",
+ " def bark(self):\n",
+ " return \"Woof!\"\n",
+ "\n",
+ "# Create an instance of the Dog class\n",
+ "my_dog = Dog(\"Buddy\", 3)\n",
+ "\n",
+ "# Print the attributes of the instance\n",
+ "print(\"my_dog name:\", my_dog.name)\n",
+ "print(\"my_dog age:\", my_dog.age)\n",
+ "\n",
+ "# Call the bark method of the instance\n",
+ "bark_result = my_dog.bark()\n",
+ "print(\"bark_result:\", bark_result)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3c4c0991",
+ "metadata": {},
+ "source": [
+ "## Input/Output and File Handling\n",
+ "\n",
+ "**Input/Output**\n",
+ "\n",
+ "```python\n",
+ "# Get user input and display it\n",
+ "user_input = input(\"Enter a number: \")\n",
+ "print(\"You entered:\", user_input)\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4a578525",
+ "metadata": {},
+ "source": [
+ "**File Handling**\n",
+ "\n",
+ "```python\n",
+ "# Read content from a file\n",
+ "with open(\"file.txt\", \"r\") as file:\n",
+ " content = file.read()\n",
+ "\n",
+ "# Write content to a new file\n",
+ "with open(\"new_file.txt\", \"w\") as new_file:\n",
+ " new_file.write(\"Hello, world!\")\n",
+ "```"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/python/st_python.ipynb b/docs/examples/python/st_python.ipynb
new file mode 100644
index 0000000..a697fb7
--- /dev/null
+++ b/docs/examples/python/st_python.ipynb
@@ -0,0 +1,434 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "09a09de4",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ " \n",
+ "# Python Cheat Sheet"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cfb79b3a",
+ "metadata": {},
+ "source": [
+ "\n",
+ " ](https://streamlit.io/)'''.format(\n",
+ " img_to_bytes(\"https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/python/python.png\")), unsafe_allow_html=True)\n",
+ "\n",
+ " st.sidebar.header('Python Cheat Sheet')\n",
+ " st.sidebar.markdown('''\n",
+ "[Python](https://www.python.org/) is a high-level, interpreted programming\n",
+ " language known for its simplicity, readability, and versatility. \n",
+ " Created by Guido van Rossum and first released in 1991,\n",
+ " Python has gained immense popularity in various domains,\n",
+ " including web development, data science, automation, and more.\n",
+ " ''', unsafe_allow_html=True)\n",
+ "\n",
+ " # why python ?\n",
+ " st.sidebar.markdown('__Why Python?__')\n",
+ " st.sidebar.markdown('''\n",
+ " Python has experienced a remarkable surge in popularity over the years and has become one of the most \n",
+ " widely used programming languages across various fields. ''', unsafe_allow_html=True)\n",
+ "\n",
+ " st.sidebar.markdown(\n",
+ " '''[](https://streamlit.io/)'''.format(\n",
+ " img_to_bytes(\"https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/python/survey2.png\")), unsafe_allow_html=True)\n",
+ "\n",
+ " return None\n",
+ "\n",
+ "\n",
+ "# Define the cs_body() function\n",
+ "def cs_body():\n",
+ " \"\"\"\n",
+ " Create content sections for the main body of the Streamlit cheat sheet with Python examples.\n",
+ " \"\"\"\n",
+ " col1, col2, col3 = st.columns(3) # Create columns for layout\n",
+ "\n",
+ " #######################################\n",
+ " # COLUMN 1\n",
+ " #######################################\n",
+ "\n",
+ " # Hello, World!\n",
+ " col1.subheader('Hello, World!')\n",
+ " col1.code('''\n",
+ " print(\"Hello, World!\")\n",
+ " ''')\n",
+ "\n",
+ " # Comments\n",
+ " col1.subheader('Comments')\n",
+ " col1.code('''\n",
+ " # This is a comment\n",
+ " ''')\n",
+ "\n",
+ " # Variables and Data Types\n",
+ " col1.subheader('Variables and Data Types')\n",
+ " col1.code('''\n",
+ " x = 5 # Integer\n",
+ " y = 3.14 # Float\n",
+ " name = \"John\" # String\n",
+ " is_student = True # Boolean\n",
+ " ''')\n",
+ "\n",
+ " # Basic Operations\n",
+ " col1.subheader('Basic Operations')\n",
+ " col1.code('''\n",
+ " # Perform basic arithmetic operations\n",
+ " sum_result = x + y\n",
+ " sub_result = x - y\n",
+ " mul_result = x * y\n",
+ " div_result = x / y\n",
+ " ''')\n",
+ "\n",
+ "\n",
+ " # String Operations\n",
+ " col1.subheader('String Operations')\n",
+ " col1.code('''\n",
+ " # String concatenation\n",
+ " full_name = name + \" Doe\"\n",
+ "\n",
+ " # Formatted string\n",
+ " formatted_string = f\"Hello, {name}!\"\n",
+ " ''')\n",
+ "\n",
+ " # Input and Output\n",
+ " col1.subheader('Input and Output')\n",
+ " col1.code('''\n",
+ " # Get user input and display it\n",
+ " user_input = input(\"Enter a number: \")\n",
+ " print(\"You entered:\", user_input)\n",
+ " ''')\n",
+ "\n",
+ " # File Handling\n",
+ " col1.subheader('File Handling')\n",
+ " col1.code('''\n",
+ " # Read content from a file\n",
+ " with open(\"file.txt\", \"r\") as file:\n",
+ " content = file.read()\n",
+ "\n",
+ " # Write content to a new file\n",
+ " with open(\"new_file.txt\", \"w\") as new_file:\n",
+ " new_file.write(\"Hello, world!\")\n",
+ " ''')\n",
+ "\n",
+ " # Conditional Statements\n",
+ " col1.subheader('Conditional Statements')\n",
+ " col1.code('''\n",
+ " # Check if x is greater than y\n",
+ " if x > y:\n",
+ " print(\"x is greater\")\n",
+ " # If not, check if x is less than y\n",
+ " elif x < y:\n",
+ " print(\"y is greater\")\n",
+ " # If neither condition is true, they must be equal\n",
+ " else:\n",
+ " print(\"x and y are equal\")\n",
+ " ''')\n",
+ "\n",
+ " #######################################\n",
+ " # COLUMN 2\n",
+ " #######################################\n",
+ "\n",
+ " # Creating lists\n",
+ " col2.subheader('Creating Lists')\n",
+ " col2.code('''\n",
+ " # Create lists with [], elements separated by commas\n",
+ " x = [1, 3, 2]\n",
+ " ''')\n",
+ "\n",
+ " # List functions and methods\n",
+ " col2.subheader('List Functions and Methods')\n",
+ " col2.code('''\n",
+ " # Return a sorted copy of the list e.g., [1,2,3]\n",
+ " sorted_list = sorted(x)\n",
+ "\n",
+ " # Sorts the list in place (replaces x)\n",
+ " x.sort()\n",
+ "\n",
+ " # Reverse the order of elements in x e.g., [2,3,1]\n",
+ " reversed_list = list(reversed(x))\n",
+ "\n",
+ " # Reverse the list in place\n",
+ " x.reverse()\n",
+ "\n",
+ " # Count the number of element 2 in the list\n",
+ " count_2 = x.count(2)\n",
+ " ''')\n",
+ "\n",
+ " # Selecting list elements\n",
+ " col2.subheader('Selecting List Elements')\n",
+ " col2.code('''\n",
+ " # Select the 0th element in the list\n",
+ " element_0 = x[0]\n",
+ "\n",
+ " # Select the last element in the list\n",
+ " last_element = x[-1]\n",
+ "\n",
+ " # Select 1st (inclusive) to 3rd (exclusive)\n",
+ " subset_1_to_3 = x[1:3]\n",
+ "\n",
+ " # Select the 2nd to the end\n",
+ " subset_2_to_end = x[2:]\n",
+ "\n",
+ " # Select 0th to 3rd (exclusive)\n",
+ " subset_0_to_3 = x[:3]\n",
+ " ''')\n",
+ "\n",
+ " # Concatenating lists\n",
+ " col2.subheader('Concatenating Lists')\n",
+ " col2.code('''\n",
+ " # Define the x and y lists\n",
+ " x = [1, 3, 6]\n",
+ " y = [10, 15, 21]\n",
+ "\n",
+ " # Concatenate lists using '+'\n",
+ " concatenated_list = x + y\n",
+ "\n",
+ " # Replicate elements in a list using '*'\n",
+ " replicated_list = 3 * x\n",
+ " ''')\n",
+ "\n",
+ " # Creating dictionaries\n",
+ " col2.subheader('Creating Dictionaries')\n",
+ " col2.code('''\n",
+ " # Create a dictionary with {}\n",
+ " my_dict = {'a': 1, 'b': 2, 'c': 3}\n",
+ "\n",
+ " ''')\n",
+ "\n",
+ " # Dictionary functions and methods\n",
+ " col2.subheader('Dictionary Functions and Methods')\n",
+ " col2.code('''\n",
+ " # Get the keys of a dictionary\n",
+ " my_dict.keys() # Returns dict_keys(['a', 'b', 'c'])\n",
+ "\n",
+ " # Get the values of a dictionary\n",
+ " my_dict.values() # Returns dict_values([1, 2, 3])\n",
+ " \n",
+ " # Get a value from a dictionary by specifying the key\n",
+ " my_dict['a'] # Returns 1 \n",
+ " ''')\n",
+ "\n",
+ " #######################################\n",
+ " # COLUMN 3\n",
+ " #######################################\n",
+ "\n",
+ " # Loops\n",
+ " col3.subheader('Loops')\n",
+ " col3.code('''\n",
+ " # For loop\n",
+ " numbers = [1, 2, 3, 4, 5]\n",
+ " for num in numbers:\n",
+ " print(num)\n",
+ "\n",
+ " # While loop\n",
+ " x = 5\n",
+ " while x > 0:\n",
+ " print(x)\n",
+ " x -= 1\n",
+ " ''')\n",
+ "\n",
+ " # Error Handling\n",
+ " col3.subheader('Error Handling')\n",
+ " col3.code('''\n",
+ " # Try to perform division\n",
+ " try:\n",
+ " result = x / y # Attempt to divide x by y\n",
+ " except ZeroDivisionError as e: # If a ZeroDivisionError occurs\n",
+ " print(\"Cannot divide by zero\") \n",
+ " print(e)\n",
+ " ''')\n",
+ "\n",
+ " # List Comprehensions\n",
+ " col3.subheader('List Comprehensions')\n",
+ " col3.code('''\n",
+ " # Create a list of squared numbers using a list comprehension\n",
+ " squared_numbers = [num**2 for num in numbers]\n",
+ "\n",
+ " # Create a list of even numbers using a list comprehension with condition\n",
+ " even_numbers = [num for num in numbers if num % 2 == 0]\n",
+ " ''')\n",
+ "\n",
+ " # Functions\n",
+ " col3.subheader('Functions')\n",
+ " col3.code('''\n",
+ " # Define a function that takes a name parameter\n",
+ " def greet(name):\n",
+ " return f\"Hello, {name}!\"\n",
+ "\n",
+ " # Call the greet function with the argument \"Alice\"\n",
+ " greeting = greet(\"Alice\")\n",
+ " ''')\n",
+ "\n",
+ " # Built-in Functions\n",
+ " col3.subheader('Built-in Functions')\n",
+ " col3.code('''\n",
+ " # Get the length of a list\n",
+ " len_fruits = len(fruits)\n",
+ "\n",
+ " # Find the maximum value in a list\n",
+ " max_number = max(numbers)\n",
+ "\n",
+ " # Find the minimum value in a list\n",
+ " min_number = min(numbers)\n",
+ " ''')\n",
+ "\n",
+ " # Importing Modules\n",
+ " col3.subheader('Importing Modules')\n",
+ " col3.code('''\n",
+ " import math\n",
+ " sqrt_result = math.sqrt(x) # Calculate square root using math module\n",
+ "\n",
+ " from random import randint\n",
+ " random_number = randint(1, 10) # Generate a random number between 1 and 10\n",
+ "\n",
+ " import math\n",
+ " sqrt_result = math.sqrt(x) # Reusing the math module for another calculation\n",
+ " ''')\n",
+ "\n",
+ " # Classes and Objects\n",
+ " col3.subheader('Classes and Objects')\n",
+ " col3.code('''\n",
+ " class Dog:\n",
+ " def __init__(self, name, age):\n",
+ " self.name = name\n",
+ " self.age = age\n",
+ "\n",
+ " def bark(self):\n",
+ " return \"Woof!\"\n",
+ "\n",
+ " my_dog = Dog(\"Buddy\", 3)\n",
+ " ''')\n",
+ "\n",
+ "\n",
+ "# Run the main function if the script is executed directly\n",
+ "if __name__ == '__main__':\n",
+ " main()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b70c870e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!streamlit run app.py & npx localtunnel --port 8501"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/scikit-learn/app.py b/docs/examples/scikit-learn/app.py
new file mode 100644
index 0000000..3761604
--- /dev/null
+++ b/docs/examples/scikit-learn/app.py
@@ -0,0 +1,416 @@
+import streamlit as st
+from pathlib import Path
+import base64
+import requests
+
+
+# Initial page config
+st.set_page_config(
+ page_title='Scikit-Learn Cheat Sheet',
+ layout="wide",
+ initial_sidebar_state="expanded",
+)
+
+def main():
+ """
+ Main function to set up the Streamlit app layout.
+ """
+ cs_sidebar()
+ cs_body()
+ return None
+
+# Define img_to_bytes() function
+def img_to_bytes(img_url):
+ response = requests.get(img_url)
+ img_bytes = response.content
+ encoded = base64.b64encode(img_bytes).decode()
+ return encoded
+
+# Define the cs_sidebar() function
+def cs_sidebar():
+ """
+ Populate the sidebar with various content sections related to Scikit-learn.
+ """
+ st.sidebar.markdown(
+ '''[](https://streamlit.io/)'''.format(
+ img_to_bytes("https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/scikit-learn/scikit-learn.png")), unsafe_allow_html=True)
+
+ st.sidebar.header('Scikit-Learn Cheat Sheet')
+ st.sidebar.markdown('''
+[Scikit-learn](https://scikit-learn.org/) is an open source Python library that
+ implements a range of
+machine learning,
+ preprocessing, cross-validation and visualization
+algorithms using a unified interface.
+ ''', unsafe_allow_html=True)
+
+ # Scikit-Learn installation and import
+ st.sidebar.markdown('__Install and import Scikit-Learn__')
+ st.sidebar.code('$ pip install scikit-learn')
+ st.sidebar.code('''
+# Import Scikit-Learn convention
+>>> import sklearn
+''')
+
+ # Add the Scikit-learn example
+ st.sidebar.markdown('__Scikit-learn Example__')
+ st.sidebar.markdown(
+ '''[](https://streamlit.io/)'''.format(
+ img_to_bytes("https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/scikit-learn/sk-tree.png")), unsafe_allow_html=True)
+
+ st.sidebar.code("""
+ from sklearn import neighbors, datasets, preprocessing
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+
+# Load the Iris dataset
+iris = datasets.load_iris()
+
+# Split the dataset into features (X) and target (y)
+X, y = iris.data[:, :2], iris.target
+
+# Split the dataset into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33)
+
+# Standardize the features using StandardScaler
+scaler = preprocessing.StandardScaler().fit(X_train)
+X_train = scaler.transform(X_train)
+X_test = scaler.transform(X_test)
+
+# Create a K-Nearest Neighbors classifier
+knn = neighbors.KNeighborsClassifier(n_neighbors=5)
+
+# Train the classifier on the training data
+knn.fit(X_train, y_train)
+
+# Predict the target values on the test data
+y_pred = knn.predict(X_test)
+
+# Calculate the accuracy of the classifier
+accuracy = accuracy_score(y_test, y_pred)
+""")
+ return None
+
+
+# Define the cs_body() function
+def cs_body():
+ """
+ Create content sections for the main body of the Streamlit cheat sheet with Scikit-learn examples.
+ """
+ col1, col2, col3 = st.columns(3) # Create columns for layout
+
+ #######################################
+ # COLUMN 1
+ #######################################
+
+ # Loading The Data
+ col1.subheader('Loading The Data')
+ col1.code('''
+ from sklearn import datasets
+
+ # Load the Iris dataset
+ iris = datasets.load_iris()
+
+ # Split the dataset into features (X) and target (y)
+ X, y = iris.data, iris.target
+
+ # Print the lengths of X and y
+ print("Size of X:", X.shape) # (150, 4)
+ print("Size of y:", y.shape) # (150, )
+ ''')
+
+ # Training And Test Data
+ col1.subheader('Training And Test Data')
+ col1.code('''
+ # Import train_test_split from sklearn
+ from sklearn.model_selection import train_test_split
+
+ # Split the data into training and test sets with test_size=0.2 (20% for test set)
+ X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+ ''')
+
+ # Create instances of the models
+ col1.subheader('Create instances of the models')
+ col1.code('''
+ # Import necessary classes from sklearn libraries
+ from sklearn.linear_model import LogisticRegression
+ from sklearn.neighbors import KNeighborsClassifier
+ from sklearn.svm import SVC
+ from sklearn.cluster import KMeans
+ from sklearn.decomposition import PCA
+
+ # Create instances of supervised learning models
+ # Logistic Regression classifier (max_iter=1000)
+ lr = LogisticRegression(max_iter=1000)
+
+ # k-Nearest Neighbors classifier with 5 neighbors
+ knn = KNeighborsClassifier(n_neighbors=5)
+
+ # Support Vector Machine classifier
+ svc = SVC()
+
+ # Create instances of unsupervised learning models
+ # k-Means clustering with 3 clusters and 10 initialization attempts
+ k_means = KMeans(n_clusters=3, n_init=10)
+
+ # Principal Component Analysis with 2 components
+ pca = PCA(n_components=2)
+ ''')
+
+
+ # Model Fitting
+ col1.subheader('Model Fitting')
+ col1.code('''
+ # Supervised learning
+ lr.fit(X_train, y_train)
+ knn.fit(X_train, y_train)
+ svc.fit(X_train, y_train)
+
+ # Unsupervised Learning
+ k_means.fit(X_train)
+ pca.fit_transform(X_train)
+ ''')
+
+ # Prediction
+ col1.subheader('Prediction')
+ col1.code('''
+ # Supervised Estimators
+ y_pred = svc.predict(X_test) # Predict labels
+ y_pred = lr.predict(X_test) # Predict labels
+ y_pred = knn.predict_proba(X_test) # Estimate probability of a label
+
+ # Unsupervised Estimators
+ y_pred = k_means.predict(X_test) # Predict labels in clustering algos
+ ''')
+
+
+
+
+
+ #######################################
+ # COLUMN 2
+ #######################################
+
+ # Preprocessing The Data
+
+ # Standardization
+ col2.subheader('Standardization')
+ col2.code('''
+ from sklearn.preprocessing import StandardScaler
+
+ # Create an instance of the StandardScaler and fit it to training data
+ scaler = StandardScaler().fit(X_train)
+
+ # Transform the training and test data using the scaler
+ standardized_X = scaler.transform(X_train)
+ standardized_X_test = scaler.transform(X_test)
+ ''')
+
+ # Normalization
+ col2.subheader('Normalization')
+ col2.code('''
+ from sklearn.preprocessing import Normalizer
+ scaler = Normalizer().fit(X_train)
+ normalized_X = scaler.transform(X_train)
+ normalized_X_test = scaler.transform(X_test)
+ ''')
+
+ # Binarization
+ col2.subheader('Binarization')
+ col2.code('''
+ import numpy as np
+ from sklearn.preprocessing import Binarizer
+
+ # Create a sample data array
+ data = np.array([[1.5, 2.7, 0.8],
+ [0.2, 3.9, 1.2],
+ [4.1, 1.0, 2.5]])
+
+ # Create a Binarizer instance with a threshold of 2.0
+ binarizer = Binarizer(threshold=2.0)
+
+ # Apply binarization to the data
+ binarized_data = binarizer.transform(data)
+ ''')
+
+ # Encoding Categorical Features
+ col2.subheader('Encoding Categorical Features')
+ col2.code('''
+ from sklearn.preprocessing import LabelEncoder
+
+ # Sample data: categorical labels
+ labels = ['cat', 'dog', 'dog', 'fish', 'cat', 'dog', 'fish']
+
+ # Create a LabelEncoder instance
+ label_encoder = LabelEncoder()
+
+ # Fit and transform the labels
+ encoded_labels = label_encoder.fit_transform(labels)
+ ''')
+
+ # Imputing Missing Values
+ col2.subheader('Imputing Missing Values')
+ col2.code('''
+ import numpy as np
+ from sklearn.impute import SimpleImputer
+
+ # Sample data with missing values
+ data = np.array([[1.0, 2.0, np.nan],
+ [4.0, np.nan, 6.0],
+ [7.0, 8.0, 9.0]])
+
+ # Create a SimpleImputer instance with strategy='mean'
+ imputer = SimpleImputer(strategy='mean')
+
+ # Fit and transform the imputer on the data
+ imputed_data = imputer.fit_transform(data)
+ ''')
+
+ # Generating Polynomial Features
+ col2.subheader('Generating Polynomial Features')
+ col2.code('''
+ import numpy as np
+ from sklearn.preprocessing import PolynomialFeatures
+
+ # Sample data
+ data = np.array([[1, 2],
+ [3, 4],
+ [5, 6]])
+
+ # Create a PolynomialFeatures instance of degree 2
+ poly = PolynomialFeatures(degree=2)
+
+ # Transform the data to include polynomial features
+ poly_data = poly.fit_transform(data)
+ ''')
+
+
+
+ #######################################
+ # COLUMN 3
+ #######################################
+
+ # Comparison operations
+ # Classification Metrics
+ col3.subheader('Classification Metrics')
+ col3.code('''
+ from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+
+ # Accuracy Score
+ accuracy_knn = knn.score(X_test, y_test)
+ print("Accuracy Score (knn):", knn.score(X_test, y_test))
+
+ accuracy_y_pred = accuracy_score(y_test, y_pred_lr)
+ print("Accuracy Score (y_pred):", accuracy_y_pred)
+
+ # Classification Report
+ classification_rep_y_pred = classification_report(y_test, y_pred_lr)
+ print("Classification Report (y_pred):", classification_rep_y_pred)
+
+ classification_rep_y_pred_lr = classification_report(y_test, y_pred_lr)
+ print("Classification Report (y_pred_lr):", classification_rep_y_pred_lr)
+
+ # Confusion Matrix
+ conf_matrix_y_pred_lr = confusion_matrix(y_test, y_pred_lr)
+ print("Confusion Matrix (y_pred_lr):", conf_matrix_y_pred_lr)
+ ''')
+
+ # Regression Metrics
+ col3.subheader('Regression Metrics')
+ col3.code('''
+ from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+
+ # Data: True/Predicted values
+ y_true = [3, -0.5, 2]
+ y_pred = [2.8, -0.3, 1.8]
+
+ # Calculate Mean Absolute Error
+ mae = mean_absolute_error(y_true, y_pred)
+ print("Mean Absolute Error:", mae)
+
+ # Calculate Mean Squared Error
+ mse = mean_squared_error(y_true, y_pred)
+ print("Mean Squared Error:", mse)
+
+ # Calculate R² Score
+ r2 = r2_score(y_true, y_pred)
+ print("R² Score:", r2)
+ ''')
+
+ # Clustering Metrics
+ col3.subheader('Clustering Metrics')
+ col3.code('''
+ from sklearn.metrics import adjusted_rand_score, homogeneity_score, v_measure_score
+
+ # Adjusted Rand Index
+ adjusted_rand_index = adjusted_rand_score(y_test, y_pred_kmeans)
+ print("Adjusted Rand Index:", adjusted_rand_index)
+
+ # Homogeneity Score
+ homogeneity = homogeneity_score(y_test, y_pred_kmeans)
+ print("Homogeneity Score:", homogeneity)
+
+ # V-Measure Score
+ v_measure = v_measure_score(y_test, y_pred_kmeans)
+ print("V-Measure Score:", v_measure)
+ ''')
+
+ # Cross-Validation
+ col3.subheader('Cross-Validation')
+ col3.code('''
+ # Import necessary library
+ from sklearn.model_selection import cross_val_score
+
+ # Cross-validation with KNN estimator
+ knn_scores = cross_val_score(knn, X_train, y_train, cv=4)
+ print(knn_scores)
+
+ # Cross-validation with Linear Regression estimator
+ lr_scores = cross_val_score(lr, X, y, cv=2)
+ print(lr_scores)
+ ''')
+
+ # Grid Search
+ col3.subheader('Grid Search')
+ col3.code('''
+ # Import necessary library
+ from sklearn.model_selection import GridSearchCV
+
+ # Define parameter grid
+ params = {
+ 'n_neighbors': np.arange(1, 3),
+ 'weights': ['uniform', 'distance']
+ }
+
+ # Create GridSearchCV object
+ grid = GridSearchCV(estimator=knn, param_grid=params)
+
+ # Fit the grid to the data
+ grid.fit(X_train, y_train)
+
+ # Print the best parameters found
+ print("Best parameters:", grid.best_params_)
+
+ # Print the best cross-validation score
+ print("Best cross-validation score:", grid.best_score_)
+
+ # Print the accuracy on the test set using the best parameters
+ best_knn = grid.best_estimator_
+ test_accuracy = best_knn.score(X_test, y_test)
+ print("Test set accuracy:", test_accuracy)
+ ''')
+
+ # Asking for Help
+ col1.subheader('Asking for Help')
+ col1.code('''
+ import sklearn.cluster
+
+ # Use the help() function to get information about the KMeans class
+ help(sklearn.cluster.KMeans)
+ ''')
+
+
+
+# Run the main function if the script is executed directly
+if __name__ == '__main__':
+ main()
diff --git a/docs/examples/scikit-learn/sklearn.ipynb b/docs/examples/scikit-learn/sklearn.ipynb
new file mode 100644
index 0000000..17125d1
--- /dev/null
+++ b/docs/examples/scikit-learn/sklearn.ipynb
@@ -0,0 +1,889 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "e0df1e26",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "\n",
+ "# Scikit-Learn Cheat Sheet\n",
+ "\n",
+ "![\"numpy](\"https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/scikit-learn/scikit-learn.png\")
\n",
+ "\n",
+ "[Scikit-learn](https://scikit-learn.org/) is an open source Python library that\n",
+ " implements a range of\n",
+ "machine learning,\n",
+ " preprocessing, cross-validation and visualization\n",
+ "algorithms using a unified interface."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "22e2ffce",
+ "metadata": {},
+ "source": [
+ "## Install and import Scikit-Learn\n",
+ "\n",
+ "`\n",
+ "$ pip install scikit-Learn\n",
+ "`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "3a0de1af",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import Scikit-Learn convention\n",
+ "import sklearn"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6d13116a",
+ "metadata": {},
+ "source": [
+ "## Scikit-learn Example\n",
+ "\n",
+ "![\"numpy](\"https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/scikit-learn/sk-tree.png\")
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "bb151a39",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 0.631578947368421\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn import neighbors, datasets, preprocessing\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.metrics import accuracy_score\n",
+ "\n",
+ "# Load the Iris dataset\n",
+ "iris = datasets.load_iris()\n",
+ "\n",
+ "# Split the dataset into features (X) and target (y)\n",
+ "X, y = iris.data[:, :2], iris.target\n",
+ "\n",
+ "# Split the dataset into training and testing sets\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33)\n",
+ "\n",
+ "# Standardize the features using StandardScaler\n",
+ "scaler = preprocessing.StandardScaler().fit(X_train)\n",
+ "X_train = scaler.transform(X_train)\n",
+ "X_test = scaler.transform(X_test)\n",
+ "\n",
+ "# Create a K-Nearest Neighbors classifier\n",
+ "knn = neighbors.KNeighborsClassifier(n_neighbors=5)\n",
+ "\n",
+ "# Train the classifier on the training data\n",
+ "knn.fit(X_train, y_train)\n",
+ "\n",
+ "# Predict the target values on the test data\n",
+ "y_pred = knn.predict(X_test)\n",
+ "\n",
+ "# Calculate the accuracy of the classifier\n",
+ "accuracy = accuracy_score(y_test, y_pred)\n",
+ "\n",
+ "# Print the accuracy\n",
+ "print(\"Accuracy:\", accuracy)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1844fe7b",
+ "metadata": {},
+ "source": [
+ "## Loading The Data\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "c736518f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Size of X: (150, 4)\n",
+ "Size of y: (150,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn import datasets\n",
+ "\n",
+ "# Load the Iris dataset\n",
+ "iris = datasets.load_iris()\n",
+ "\n",
+ "# Split the dataset into features (X) and target (y)\n",
+ "X, y = iris.data, iris.target\n",
+ "\n",
+ "# Print the lengths of X and y\n",
+ "print(\"Size of X:\", X.shape) # (150, 4)\n",
+ "print(\"Size of y:\", y.shape) # (150, )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b288da8f",
+ "metadata": {},
+ "source": [
+ "## Training And Test Data\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "29b47452",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Size of X_train: (120, 4)\n",
+ "Size of X_test: (30, 4)\n",
+ "Size of y_train: (120,)\n",
+ "Size of y_test: (30,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Import train_test_split from sklearn\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "\n",
+ "# Split the data into training and test sets with test_size=0.2 (20% for test set)\n",
+ "X, y = iris.data, iris.target\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+ "\n",
+ "# Print the sizes of the arrays\n",
+ "print(\"Size of X_train:\", X_train.shape)\n",
+ "print(\"Size of X_test: \", X_test.shape)\n",
+ "print(\"Size of y_train:\", y_train.shape)\n",
+ "print(\"Size of y_test: \", y_test.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e18e21fc",
+ "metadata": {},
+ "source": [
+ "## Create instances of the models\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "8559bb3f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import necessary classes from sklearn libraries\n",
+ "from sklearn.linear_model import LogisticRegression\n",
+ "from sklearn.neighbors import KNeighborsClassifier\n",
+ "from sklearn.svm import SVC\n",
+ "from sklearn.cluster import KMeans\n",
+ "from sklearn.decomposition import PCA\n",
+ "\n",
+ "# Create instances of supervised learning models\n",
+ "# Logistic Regression classifier (max_iter=1000)\n",
+ "lr = LogisticRegression(max_iter=1000)\n",
+ "\n",
+ "# k-Nearest Neighbors classifier with 5 neighbors\n",
+ "knn = KNeighborsClassifier(n_neighbors=5)\n",
+ "\n",
+ "# Support Vector Machine classifier\n",
+ "svc = SVC()\n",
+ "\n",
+ "# Create instances of unsupervised learning models\n",
+ "# k-Means clustering with 3 clusters and 10 initialization attempts\n",
+ "k_means = KMeans(n_clusters=3, n_init=10)\n",
+ "\n",
+ "# Principal Component Analysis with 2 components\n",
+ "pca = PCA(n_components=2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c8995fc3",
+ "metadata": {},
+ "source": [
+ "## Model Fitting\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "f00a391f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "lr: LogisticRegression(max_iter=1000)\n",
+ "knn: KNeighborsClassifier()\n",
+ "svc: SVC()\n",
+ "k_means: KMeans(n_clusters=3, n_init=10)\n",
+ "pca: PCA(n_components=2)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Fit models to the data\n",
+ "lr.fit(X_train, y_train)\n",
+ "knn.fit(X_train, y_train)\n",
+ "svc.fit(X_train, y_train)\n",
+ "k_means.fit(X_train)\n",
+ "pca.fit_transform(X_train)\n",
+ "\n",
+ "# Print the instances and models\n",
+ "print(\"lr:\", lr)\n",
+ "print(\"knn:\", knn)\n",
+ "print(\"svc:\", svc)\n",
+ "print(\"k_means:\", k_means)\n",
+ "print(\"pca:\", pca)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5a3a7bb0",
+ "metadata": {},
+ "source": [
+ "## Prediction\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "3954fe06",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Supervised Estimators:\n",
+ "SVC predictions: [2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0]\n",
+ "Logistic Regression predictions: [2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0]\n",
+ "KNeighborsClassifier probabilities:\n",
+ " [[0. 0. 1.]\n",
+ " [0. 1. 0.]\n",
+ " [1. 0. 0.]\n",
+ " [0. 0. 1.]\n",
+ " [1. 0. 0.]] \n",
+ " ...\n",
+ "\n",
+ "Unsupervised Estimators:\n",
+ "KMeans predictions: [2 2 0 1 0 1 0 2 2 2 1 2 2 2 2 0 2 2 0 0 2 2 0 0 2 0 0 2 2 0]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Predict using different supervised estimators\n",
+ "y_pred_svc = svc.predict(X_test)\n",
+ "y_pred_lr = lr.predict(X_test)\n",
+ "y_pred_knn_proba = knn.predict_proba(X_test)\n",
+ "\n",
+ "\n",
+ "# Predict labels using KMeans in clustering algorithms\n",
+ "y_pred_kmeans = k_means.predict(X_test)\n",
+ "\n",
+ "# Print the results\n",
+ "print(\"Supervised Estimators:\")\n",
+ "print(\"SVC predictions:\", y_pred_svc)\n",
+ "print(\"Logistic Regression predictions:\", y_pred_lr)\n",
+ "print(\"KNeighborsClassifier probabilities:\\n\", y_pred_knn_proba[:5],\"\\n ...\")\n",
+ "\n",
+ "print(\"\\nUnsupervised Estimators:\")\n",
+ "print(\"KMeans predictions:\", y_pred_kmeans)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fea556d7",
+ "metadata": {},
+ "source": [
+ "## Preprocessing The Data\n",
+ "\n",
+ "### Standardization"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "75d262ac",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Standardized X_train:\n",
+ " [[ 0.61303014 0.10850105 0.94751783 0.736072 ]\n",
+ " [-0.56776627 -0.12400121 0.38491447 0.34752959]\n",
+ " [-0.80392556 1.03851009 -1.30289562 -1.33615415]\n",
+ " [ 0.25879121 -0.12400121 0.60995581 0.736072 ]\n",
+ " [ 0.61303014 -0.58900572 1.00377816 1.25412853]] \n",
+ " ...\n",
+ "\n",
+ "Standardized X_test:\n",
+ " [[-0.09544771 -0.58900572 0.72247648 1.5131568 ]\n",
+ " [ 0.14071157 -1.98401928 0.10361279 -0.30004108]\n",
+ " [-0.44968663 2.66602591 -1.35915595 -1.33615415]\n",
+ " [ 1.6757469 -0.35650346 1.39760052 0.736072 ]\n",
+ " [-1.04008484 0.80600783 -1.30289562 -1.33615415]] \n",
+ " ...\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.preprocessing import StandardScaler\n",
+ "\n",
+ "# Create an instance of the StandardScaler and fit it to training data\n",
+ "scaler = StandardScaler().fit(X_train)\n",
+ "\n",
+ "# Transform the training and test data using the scaler\n",
+ "standardized_X = scaler.transform(X_train)\n",
+ "standardized_X_test = scaler.transform(X_test)\n",
+ "\n",
+ "# Print the variables\n",
+ "print(\"\\nStandardized X_train:\\n\", standardized_X[:5],\"\\n ...\")\n",
+ "print(\"\\nStandardized X_test:\\n\", standardized_X_test[:5],\"\\n ...\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b58eed58",
+ "metadata": {},
+ "source": [
+ "### Normalization"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "32614426",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Normalized X_train:\n",
+ " [[0.69804799 0.338117 0.59988499 0.196326 ]\n",
+ " [0.69333409 0.38518561 0.57777841 0.1925928 ]\n",
+ " [0.80641965 0.54278246 0.23262105 0.03101614]\n",
+ " [0.71171214 0.35002236 0.57170319 0.21001342]\n",
+ " [0.69417747 0.30370264 0.60740528 0.2386235 ]] \n",
+ " ...\n",
+ "\n",
+ "Normalized X_test:\n",
+ " [[0.67767924 0.32715549 0.59589036 0.28041899]\n",
+ " [0.78892752 0.28927343 0.52595168 0.13148792]\n",
+ " [0.77867447 0.59462414 0.19820805 0.02831544]\n",
+ " [0.71366557 0.28351098 0.61590317 0.17597233]\n",
+ " [0.80218492 0.54548574 0.24065548 0.0320874 ]] \n",
+ " ...\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.preprocessing import Normalizer\n",
+ "scaler = Normalizer().fit(X_train)\n",
+ "normalized_X = scaler.transform(X_train)\n",
+ "normalized_X_test = scaler.transform(X_test)\n",
+ "\n",
+ "# Print the variables\n",
+ "print(\"\\nNormalized X_train:\\n\", normalized_X[:5],\"\\n ...\")\n",
+ "print(\"\\nNormalized X_test:\\n\", normalized_X_test[:5],\"\\n ...\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e9209670",
+ "metadata": {},
+ "source": [
+ "### Binarization"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "0a6367d2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Original data:\n",
+ "[[1.5 2.7 0.8]\n",
+ " [0.2 3.9 1.2]\n",
+ " [4.1 1. 2.5]]\n",
+ "\n",
+ "Binarized data:\n",
+ "[[0. 1. 0.]\n",
+ " [0. 1. 0.]\n",
+ " [1. 0. 1.]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "from sklearn.preprocessing import Binarizer\n",
+ "\n",
+ "# Create a sample data array\n",
+ "data = np.array([[1.5, 2.7, 0.8],\n",
+ " [0.2, 3.9, 1.2],\n",
+ " [4.1, 1.0, 2.5]])\n",
+ "\n",
+ "# Create a Binarizer instance with a threshold of 2.0\n",
+ "binarizer = Binarizer(threshold=2.0)\n",
+ "\n",
+ "# Apply binarization to the data\n",
+ "binarized_data = binarizer.transform(data)\n",
+ "\n",
+ "print(\"Original data:\")\n",
+ "print(data)\n",
+ "print(\"\\nBinarized data:\")\n",
+ "print(binarized_data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b4196cfd",
+ "metadata": {},
+ "source": [
+ "### Encoding Categorical Features\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "b1233524",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Original labels: ['cat', 'dog', 'dog', 'fish', 'cat', 'dog', 'fish']\n",
+ "Encoded labels: [0 1 1 2 0 1 2]\n",
+ "Decoded labels: ['cat' 'dog' 'dog' 'fish' 'cat' 'dog' 'fish']\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.preprocessing import LabelEncoder\n",
+ "\n",
+ "# Sample data: categorical labels\n",
+ "labels = ['cat', 'dog', 'dog', 'fish', 'cat', 'dog', 'fish']\n",
+ "\n",
+ "# Create a LabelEncoder instance\n",
+ "label_encoder = LabelEncoder()\n",
+ "\n",
+ "# Fit and transform the labels\n",
+ "encoded_labels = label_encoder.fit_transform(labels)\n",
+ "\n",
+ "# Print the original labels and their encoded versions\n",
+ "print(\"Original labels:\", labels)\n",
+ "print(\"Encoded labels:\", encoded_labels)\n",
+ "\n",
+ "# Decode the encoded labels back to the original labels\n",
+ "decoded_labels = label_encoder.inverse_transform(encoded_labels)\n",
+ "print(\"Decoded labels:\", decoded_labels)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "44306b3f",
+ "metadata": {},
+ "source": [
+ "### Imputing Missing Values\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "efa74719",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Original data:\n",
+ "[[ 1. 2. nan]\n",
+ " [ 4. nan 6.]\n",
+ " [ 7. 8. 9.]]\n",
+ "\n",
+ "Imputed data:\n",
+ "[[1. 2. 7.5]\n",
+ " [4. 5. 6. ]\n",
+ " [7. 8. 9. ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "from sklearn.impute import SimpleImputer\n",
+ "\n",
+ "# Sample data with missing values\n",
+ "data = np.array([[1.0, 2.0, np.nan],\n",
+ " [4.0, np.nan, 6.0],\n",
+ " [7.0, 8.0, 9.0]])\n",
+ "\n",
+ "# Create a SimpleImputer instance with strategy='mean'\n",
+ "imputer = SimpleImputer(strategy='mean')\n",
+ "\n",
+ "# Fit and transform the imputer on the data\n",
+ "imputed_data = imputer.fit_transform(data)\n",
+ "\n",
+ "print(\"Original data:\")\n",
+ "print(data)\n",
+ "print(\"\\nImputed data:\")\n",
+ "print(imputed_data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "82c0374a",
+ "metadata": {},
+ "source": [
+ "### Generating Polynomial Features"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "55404919",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Original data:\n",
+ "[[1 2]\n",
+ " [3 4]\n",
+ " [5 6]]\n",
+ "\n",
+ "Polynomial features:\n",
+ "[[ 1. 1. 2. 1. 2. 4.]\n",
+ " [ 1. 3. 4. 9. 12. 16.]\n",
+ " [ 1. 5. 6. 25. 30. 36.]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "from sklearn.preprocessing import PolynomialFeatures\n",
+ "\n",
+ "# Sample data\n",
+ "data = np.array([[1, 2],\n",
+ " [3, 4],\n",
+ " [5, 6]])\n",
+ "\n",
+ "# Create a PolynomialFeatures instance of degree 2\n",
+ "poly = PolynomialFeatures(degree=2)\n",
+ "\n",
+ "# Transform the data to include polynomial features\n",
+ "poly_data = poly.fit_transform(data)\n",
+ "\n",
+ "print(\"Original data:\")\n",
+ "print(data)\n",
+ "print(\"\\nPolynomial features:\")\n",
+ "print(poly_data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f9ee4f6c",
+ "metadata": {},
+ "source": [
+ "## Classification Metrics\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "fcd4d4a0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy Score (knn): 0.9666666666666667\n",
+ "Accuracy Score (y_pred): 1.0\n",
+ "Classification Report (y_pred):\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 1.00 1.00 1.00 11\n",
+ " 1 1.00 1.00 1.00 13\n",
+ " 2 1.00 1.00 1.00 6\n",
+ "\n",
+ " accuracy 1.00 30\n",
+ " macro avg 1.00 1.00 1.00 30\n",
+ "weighted avg 1.00 1.00 1.00 30\n",
+ "\n",
+ "Classification Report (y_pred_lr):\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 1.00 1.00 1.00 11\n",
+ " 1 1.00 1.00 1.00 13\n",
+ " 2 1.00 1.00 1.00 6\n",
+ "\n",
+ " accuracy 1.00 30\n",
+ " macro avg 1.00 1.00 1.00 30\n",
+ "weighted avg 1.00 1.00 1.00 30\n",
+ "\n",
+ "Confusion Matrix (y_pred_lr):\n",
+ " [[11 0 0]\n",
+ " [ 0 13 0]\n",
+ " [ 0 0 6]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
+ "\n",
+ "# Accuracy Score\n",
+ "accuracy_knn = knn.score(X_test, y_test)\n",
+ "print(\"Accuracy Score (knn):\", knn.score(X_test, y_test))\n",
+ "\n",
+ "accuracy_y_pred = accuracy_score(y_test, y_pred_lr)\n",
+ "print(\"Accuracy Score (y_pred):\", accuracy_y_pred)\n",
+ "\n",
+ "# Classification Report\n",
+ "classification_rep_y_pred = classification_report(y_test, y_pred_lr)\n",
+ "print(\"Classification Report (y_pred):\\n\", classification_rep_y_pred)\n",
+ "\n",
+ "classification_rep_y_pred_lr = classification_report(y_test, y_pred_lr)\n",
+ "print(\"Classification Report (y_pred_lr):\\n\", classification_rep_y_pred_lr)\n",
+ "\n",
+ "# Confusion Matrix\n",
+ "conf_matrix_y_pred_lr = confusion_matrix(y_test, y_pred_lr)\n",
+ "print(\"Confusion Matrix (y_pred_lr):\\n\", conf_matrix_y_pred_lr)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d172e271",
+ "metadata": {},
+ "source": [
+ "## Regression Metrics\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "0c952c7c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mean Absolute Error: 0.20000000000000004\n",
+ "Mean Squared Error: 0.040000000000000015\n",
+ "R² Score: 0.9815384615384616\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+ "\n",
+ "# True values (ground truth)\n",
+ "y_true = [3, -0.5, 2]\n",
+ "\n",
+ "# Predicted values\n",
+ "y_pred = [2.8, -0.3, 1.8]\n",
+ "\n",
+ "# Calculate Mean Absolute Error\n",
+ "mae = mean_absolute_error(y_true, y_pred)\n",
+ "print(\"Mean Absolute Error:\", mae)\n",
+ "\n",
+ "# Calculate Mean Squared Error\n",
+ "mse = mean_squared_error(y_true, y_pred)\n",
+ "print(\"Mean Squared Error:\", mse)\n",
+ "\n",
+ "# Calculate R² Score\n",
+ "r2 = r2_score(y_true, y_pred)\n",
+ "print(\"R² Score:\", r2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1ca9d3c1",
+ "metadata": {},
+ "source": [
+ "## Clustering Metrics\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "162f3aea",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Adjusted Rand Index: 0.7657144139494176\n",
+ "Homogeneity Score: 0.7553796021571243\n",
+ "V-Measure Score: 0.8005552543570766\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import adjusted_rand_score, homogeneity_score, v_measure_score\n",
+ "\n",
+ "# Adjusted Rand Index\n",
+ "adjusted_rand_index = adjusted_rand_score(y_test, y_pred_kmeans)\n",
+ "print(\"Adjusted Rand Index:\", adjusted_rand_index)\n",
+ "\n",
+ "# Homogeneity Score\n",
+ "homogeneity = homogeneity_score(y_test, y_pred_kmeans)\n",
+ "print(\"Homogeneity Score:\", homogeneity)\n",
+ "\n",
+ "# V-Measure Score\n",
+ "v_measure = v_measure_score(y_test, y_pred_kmeans)\n",
+ "print(\"V-Measure Score:\", v_measure)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3d91e042",
+ "metadata": {},
+ "source": [
+ "## Cross-Validation\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "73d8f06c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[0.96666667 0.93333333 1. 0.93333333]\n",
+ "[0.96 0.96]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Import necessary library\n",
+ "from sklearn.model_selection import cross_val_score\n",
+ "\n",
+ "# Cross-validation with KNN estimator\n",
+ "knn_scores = cross_val_score(knn, X_train, y_train, cv=4)\n",
+ "print(knn_scores)\n",
+ "\n",
+ "# Cross-validation with Linear Regression estimator\n",
+ "lr_scores = cross_val_score(lr, X, y, cv=2)\n",
+ "print(lr_scores)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b948b913",
+ "metadata": {},
+ "source": [
+ "## Grid Search"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "b8ee5a12",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best parameters: {'n_neighbors': 1, 'weights': 'uniform'}\n",
+ "Best cross-validation score: 0.9416666666666667\n",
+ "Test set accuracy: 1.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Import necessary library\n",
+ "from sklearn.model_selection import GridSearchCV\n",
+ "\n",
+ "# Define parameter grid\n",
+ "params = {\n",
+ " 'n_neighbors': np.arange(1, 3),\n",
+ " 'weights': ['uniform', 'distance']\n",
+ "}\n",
+ "\n",
+ "# Create GridSearchCV object\n",
+ "grid = GridSearchCV(estimator=knn, param_grid=params)\n",
+ "\n",
+ "# Fit the grid to the data\n",
+ "grid.fit(X_train, y_train)\n",
+ "\n",
+ "# Print the best parameters found\n",
+ "print(\"Best parameters:\", grid.best_params_)\n",
+ "\n",
+ "# Print the best cross-validation score\n",
+ "print(\"Best cross-validation score:\", grid.best_score_)\n",
+ "\n",
+ "# Print the accuracy on the test set using the best parameters\n",
+ "best_knn = grid.best_estimator_\n",
+ "test_accuracy = best_knn.score(X_test, y_test)\n",
+ "print(\"Test set accuracy:\", test_accuracy)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/scikit-learn/st_sklearn.ipynb b/docs/examples/scikit-learn/st_sklearn.ipynb
new file mode 100644
index 0000000..803428c
--- /dev/null
+++ b/docs/examples/scikit-learn/st_sklearn.ipynb
@@ -0,0 +1,525 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "f6ec082e",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ " \n",
+ "# Scikit-Learn Cheat Sheet"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7aaaca45",
+ "metadata": {},
+ "source": [
+ "\n",
+ " ](https://streamlit.io/)'''.format(\n",
+ " img_to_bytes(\"https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/scikit-learn/scikit-learn.png\")), unsafe_allow_html=True)\n",
+ "\n",
+ " st.sidebar.header('Scikit-Learn Cheat Sheet')\n",
+ " st.sidebar.markdown('''\n",
+ "[Scikit-learn](https://scikit-learn.org/) is an open source Python library that\n",
+ " implements a range of\n",
+ "machine learning,\n",
+ " preprocessing, cross-validation and visualization\n",
+ "algorithms using a unified interface.\n",
+ " ''', unsafe_allow_html=True)\n",
+ "\n",
+ " # Scikit-Learn installation and import\n",
+ " st.sidebar.markdown('__Install and import Scikit-Learn__')\n",
+ " st.sidebar.code('$ pip install scikit-learn')\n",
+ " st.sidebar.code('''\n",
+ "# Import Scikit-Learn convention\n",
+ ">>> import sklearn\n",
+ "''')\n",
+ "\n",
+ " # Add the Scikit-learn example\n",
+ " st.sidebar.markdown('__Scikit-learn Example__')\n",
+ " st.sidebar.markdown(\n",
+ " '''[](https://streamlit.io/)'''.format(\n",
+ " img_to_bytes(\"https://raw.githubusercontent.com/fralfaro/DS-Cheat-Sheets/main/docs/examples/scikit-learn/sk-tree.png\")), unsafe_allow_html=True)\n",
+ "\n",
+ " st.sidebar.code(\"\"\"\n",
+ " from sklearn import neighbors, datasets, preprocessing\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.metrics import accuracy_score\n",
+ "\n",
+ "# Load the Iris dataset\n",
+ "iris = datasets.load_iris()\n",
+ "\n",
+ "# Split the dataset into features (X) and target (y)\n",
+ "X, y = iris.data[:, :2], iris.target\n",
+ "\n",
+ "# Split the dataset into training and testing sets\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33)\n",
+ "\n",
+ "# Standardize the features using StandardScaler\n",
+ "scaler = preprocessing.StandardScaler().fit(X_train)\n",
+ "X_train = scaler.transform(X_train)\n",
+ "X_test = scaler.transform(X_test)\n",
+ "\n",
+ "# Create a K-Nearest Neighbors classifier\n",
+ "knn = neighbors.KNeighborsClassifier(n_neighbors=5)\n",
+ "\n",
+ "# Train the classifier on the training data\n",
+ "knn.fit(X_train, y_train)\n",
+ "\n",
+ "# Predict the target values on the test data\n",
+ "y_pred = knn.predict(X_test)\n",
+ "\n",
+ "# Calculate the accuracy of the classifier\n",
+ "accuracy = accuracy_score(y_test, y_pred)\n",
+ "\"\"\")\n",
+ " return None\n",
+ "\n",
+ "\n",
+ "# Define the cs_body() function\n",
+ "def cs_body():\n",
+ " \"\"\"\n",
+ " Create content sections for the main body of the Streamlit cheat sheet with Scikit-learn examples.\n",
+ " \"\"\"\n",
+ " col1, col2, col3 = st.columns(3) # Create columns for layout\n",
+ "\n",
+ " #######################################\n",
+ " # COLUMN 1\n",
+ " #######################################\n",
+ "\n",
+ " # Loading The Data\n",
+ " col1.subheader('Loading The Data')\n",
+ " col1.code('''\n",
+ " from sklearn import datasets\n",
+ "\n",
+ " # Load the Iris dataset\n",
+ " iris = datasets.load_iris()\n",
+ "\n",
+ " # Split the dataset into features (X) and target (y)\n",
+ " X, y = iris.data, iris.target\n",
+ "\n",
+ " # Print the lengths of X and y\n",
+ " print(\"Size of X:\", X.shape) # (150, 4)\n",
+ " print(\"Size of y:\", y.shape) # (150, )\n",
+ " ''')\n",
+ "\n",
+ " # Training And Test Data\n",
+ " col1.subheader('Training And Test Data')\n",
+ " col1.code('''\n",
+ " # Import train_test_split from sklearn\n",
+ " from sklearn.model_selection import train_test_split\n",
+ "\n",
+ " # Split the data into training and test sets with test_size=0.2 (20% for test set)\n",
+ " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+ " ''')\n",
+ "\n",
+ " # Create instances of the models\n",
+ " col1.subheader('Create instances of the models')\n",
+ " col1.code('''\n",
+ " # Import necessary classes from sklearn libraries\n",
+ " from sklearn.linear_model import LogisticRegression\n",
+ " from sklearn.neighbors import KNeighborsClassifier\n",
+ " from sklearn.svm import SVC\n",
+ " from sklearn.cluster import KMeans\n",
+ " from sklearn.decomposition import PCA\n",
+ "\n",
+ " # Create instances of supervised learning models\n",
+ " # Logistic Regression classifier (max_iter=1000)\n",
+ " lr = LogisticRegression(max_iter=1000)\n",
+ "\n",
+ " # k-Nearest Neighbors classifier with 5 neighbors\n",
+ " knn = KNeighborsClassifier(n_neighbors=5)\n",
+ "\n",
+ " # Support Vector Machine classifier\n",
+ " svc = SVC()\n",
+ "\n",
+ " # Create instances of unsupervised learning models\n",
+ " # k-Means clustering with 3 clusters and 10 initialization attempts\n",
+ " k_means = KMeans(n_clusters=3, n_init=10)\n",
+ "\n",
+ " # Principal Component Analysis with 2 components\n",
+ " pca = PCA(n_components=2)\n",
+ " ''')\n",
+ "\n",
+ "\n",
+ " # Model Fitting\n",
+ " col1.subheader('Model Fitting')\n",
+ " col1.code('''\n",
+ " # Supervised learning\n",
+ " lr.fit(X_train, y_train)\n",
+ " knn.fit(X_train, y_train)\n",
+ " svc.fit(X_train, y_train)\n",
+ "\n",
+ " # Unsupervised Learning\n",
+ " k_means.fit(X_train)\n",
+ " pca.fit_transform(X_train)\n",
+ " ''')\n",
+ "\n",
+ " # Prediction\n",
+ " col1.subheader('Prediction')\n",
+ " col1.code('''\n",
+ " # Supervised Estimators\n",
+ " y_pred = svc.predict(X_test) # Predict labels\n",
+ " y_pred = lr.predict(X_test) # Predict labels\n",
+ " y_pred = knn.predict_proba(X_test) # Estimate probability of a label\n",
+ "\n",
+ " # Unsupervised Estimators\n",
+ " y_pred = k_means.predict(X_test) # Predict labels in clustering algos\n",
+ " ''')\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ " #######################################\n",
+ " # COLUMN 2\n",
+ " #######################################\n",
+ "\n",
+ " # Preprocessing The Data\n",
+ "\n",
+ " # Standardization\n",
+ " col2.subheader('Standardization')\n",
+ " col2.code('''\n",
+ " from sklearn.preprocessing import StandardScaler\n",
+ "\n",
+ " # Create an instance of the StandardScaler and fit it to training data\n",
+ " scaler = StandardScaler().fit(X_train)\n",
+ "\n",
+ " # Transform the training and test data using the scaler\n",
+ " standardized_X = scaler.transform(X_train)\n",
+ " standardized_X_test = scaler.transform(X_test)\n",
+ " ''')\n",
+ "\n",
+ " # Normalization\n",
+ " col2.subheader('Normalization')\n",
+ " col2.code('''\n",
+ " from sklearn.preprocessing import Normalizer\n",
+ " scaler = Normalizer().fit(X_train)\n",
+ " normalized_X = scaler.transform(X_train)\n",
+ " normalized_X_test = scaler.transform(X_test)\n",
+ " ''')\n",
+ "\n",
+ " # Binarization\n",
+ " col2.subheader('Binarization')\n",
+ " col2.code('''\n",
+ " import numpy as np\n",
+ " from sklearn.preprocessing import Binarizer\n",
+ "\n",
+ " # Create a sample data array\n",
+ " data = np.array([[1.5, 2.7, 0.8],\n",
+ " [0.2, 3.9, 1.2],\n",
+ " [4.1, 1.0, 2.5]])\n",
+ "\n",
+ " # Create a Binarizer instance with a threshold of 2.0\n",
+ " binarizer = Binarizer(threshold=2.0)\n",
+ "\n",
+ " # Apply binarization to the data\n",
+ " binarized_data = binarizer.transform(data)\n",
+ " ''')\n",
+ "\n",
+ " # Encoding Categorical Features\n",
+ " col2.subheader('Encoding Categorical Features')\n",
+ " col2.code('''\n",
+ " from sklearn.preprocessing import LabelEncoder\n",
+ "\n",
+ " # Sample data: categorical labels\n",
+ " labels = ['cat', 'dog', 'dog', 'fish', 'cat', 'dog', 'fish']\n",
+ "\n",
+ " # Create a LabelEncoder instance\n",
+ " label_encoder = LabelEncoder()\n",
+ "\n",
+ " # Fit and transform the labels\n",
+ " encoded_labels = label_encoder.fit_transform(labels)\n",
+ " ''')\n",
+ "\n",
+ " # Imputing Missing Values\n",
+ " col2.subheader('Imputing Missing Values')\n",
+ " col2.code('''\n",
+ " import numpy as np\n",
+ " from sklearn.impute import SimpleImputer\n",
+ "\n",
+ " # Sample data with missing values\n",
+ " data = np.array([[1.0, 2.0, np.nan],\n",
+ " [4.0, np.nan, 6.0],\n",
+ " [7.0, 8.0, 9.0]])\n",
+ "\n",
+ " # Create a SimpleImputer instance with strategy='mean'\n",
+ " imputer = SimpleImputer(strategy='mean')\n",
+ "\n",
+ " # Fit and transform the imputer on the data\n",
+ " imputed_data = imputer.fit_transform(data)\n",
+ " ''')\n",
+ "\n",
+ " # Generating Polynomial Features\n",
+ " col2.subheader('Generating Polynomial Features')\n",
+ " col2.code('''\n",
+ " import numpy as np\n",
+ " from sklearn.preprocessing import PolynomialFeatures\n",
+ "\n",
+ " # Sample data\n",
+ " data = np.array([[1, 2],\n",
+ " [3, 4],\n",
+ " [5, 6]])\n",
+ "\n",
+ " # Create a PolynomialFeatures instance of degree 2\n",
+ " poly = PolynomialFeatures(degree=2)\n",
+ "\n",
+ " # Transform the data to include polynomial features\n",
+ " poly_data = poly.fit_transform(data)\n",
+ " ''')\n",
+ "\n",
+ "\n",
+ "\n",
+ " #######################################\n",
+ " # COLUMN 3\n",
+ " #######################################\n",
+ "\n",
+ " # Comparison operations\n",
+ " # Classification Metrics\n",
+ " col3.subheader('Classification Metrics')\n",
+ " col3.code('''\n",
+ " from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
+ "\n",
+ " # Accuracy Score\n",
+ " accuracy_knn = knn.score(X_test, y_test)\n",
+ " print(\"Accuracy Score (knn):\", knn.score(X_test, y_test))\n",
+ "\n",
+ " accuracy_y_pred = accuracy_score(y_test, y_pred_lr)\n",
+ " print(\"Accuracy Score (y_pred):\", accuracy_y_pred)\n",
+ "\n",
+ " # Classification Report\n",
+ " classification_rep_y_pred = classification_report(y_test, y_pred_lr)\n",
+ " print(\"Classification Report (y_pred):\", classification_rep_y_pred)\n",
+ "\n",
+ " classification_rep_y_pred_lr = classification_report(y_test, y_pred_lr)\n",
+ " print(\"Classification Report (y_pred_lr):\", classification_rep_y_pred_lr)\n",
+ "\n",
+ " # Confusion Matrix\n",
+ " conf_matrix_y_pred_lr = confusion_matrix(y_test, y_pred_lr)\n",
+ " print(\"Confusion Matrix (y_pred_lr):\", conf_matrix_y_pred_lr)\n",
+ " ''')\n",
+ "\n",
+ " # Regression Metrics\n",
+ " col3.subheader('Regression Metrics')\n",
+ " col3.code('''\n",
+ " from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+ "\n",
+ " # Data: True/Predicted values \n",
+ " y_true = [3, -0.5, 2]\n",
+ " y_pred = [2.8, -0.3, 1.8]\n",
+ "\n",
+ " # Calculate Mean Absolute Error\n",
+ " mae = mean_absolute_error(y_true, y_pred)\n",
+ " print(\"Mean Absolute Error:\", mae)\n",
+ "\n",
+ " # Calculate Mean Squared Error\n",
+ " mse = mean_squared_error(y_true, y_pred)\n",
+ " print(\"Mean Squared Error:\", mse)\n",
+ "\n",
+ " # Calculate R² Score\n",
+ " r2 = r2_score(y_true, y_pred)\n",
+ " print(\"R² Score:\", r2)\n",
+ " ''')\n",
+ "\n",
+ " # Clustering Metrics\n",
+ " col3.subheader('Clustering Metrics')\n",
+ " col3.code('''\n",
+ " from sklearn.metrics import adjusted_rand_score, homogeneity_score, v_measure_score\n",
+ "\n",
+ " # Adjusted Rand Index\n",
+ " adjusted_rand_index = adjusted_rand_score(y_test, y_pred_kmeans)\n",
+ " print(\"Adjusted Rand Index:\", adjusted_rand_index)\n",
+ "\n",
+ " # Homogeneity Score\n",
+ " homogeneity = homogeneity_score(y_test, y_pred_kmeans)\n",
+ " print(\"Homogeneity Score:\", homogeneity)\n",
+ "\n",
+ " # V-Measure Score\n",
+ " v_measure = v_measure_score(y_test, y_pred_kmeans)\n",
+ " print(\"V-Measure Score:\", v_measure)\n",
+ " ''')\n",
+ "\n",
+ " # Cross-Validation\n",
+ " col3.subheader('Cross-Validation')\n",
+ " col3.code('''\n",
+ " # Import necessary library\n",
+ " from sklearn.model_selection import cross_val_score\n",
+ "\n",
+ " # Cross-validation with KNN estimator\n",
+ " knn_scores = cross_val_score(knn, X_train, y_train, cv=4)\n",
+ " print(knn_scores)\n",
+ "\n",
+ " # Cross-validation with Linear Regression estimator\n",
+ " lr_scores = cross_val_score(lr, X, y, cv=2)\n",
+ " print(lr_scores)\n",
+ " ''')\n",
+ "\n",
+ " # Grid Search\n",
+ " col3.subheader('Grid Search')\n",
+ " col3.code('''\n",
+ " # Import necessary library\n",
+ " from sklearn.model_selection import GridSearchCV\n",
+ "\n",
+ " # Define parameter grid\n",
+ " params = {\n",
+ " 'n_neighbors': np.arange(1, 3),\n",
+ " 'weights': ['uniform', 'distance']\n",
+ " }\n",
+ "\n",
+ " # Create GridSearchCV object\n",
+ " grid = GridSearchCV(estimator=knn, param_grid=params)\n",
+ "\n",
+ " # Fit the grid to the data\n",
+ " grid.fit(X_train, y_train)\n",
+ "\n",
+ " # Print the best parameters found\n",
+ " print(\"Best parameters:\", grid.best_params_)\n",
+ "\n",
+ " # Print the best cross-validation score\n",
+ " print(\"Best cross-validation score:\", grid.best_score_)\n",
+ "\n",
+ " # Print the accuracy on the test set using the best parameters\n",
+ " best_knn = grid.best_estimator_\n",
+ " test_accuracy = best_knn.score(X_test, y_test)\n",
+ " print(\"Test set accuracy:\", test_accuracy)\n",
+ " ''')\n",
+ "\n",
+ " # Asking for Help\n",
+ " col1.subheader('Asking for Help')\n",
+ " col1.code('''\n",
+ " import sklearn.cluster\n",
+ "\n",
+ " # Use the help() function to get information about the KMeans class\n",
+ " help(sklearn.cluster.KMeans)\n",
+ " ''')\n",
+ "\n",
+ "\n",
+ "\n",
+ "# Run the main function if the script is executed directly\n",
+ "if __name__ == '__main__':\n",
+ " main()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5d966598",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!streamlit run app.py & npx localtunnel --port 8501"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/streamlit/app.py b/docs/examples/streamlit/app.py
new file mode 100644
index 0000000..ba9f804
--- /dev/null
+++ b/docs/examples/streamlit/app.py
@@ -0,0 +1,394 @@
+import streamlit as st
+from pathlib import Path
+import base64
+
+# Initial page config
+st.set_page_config(
+ page_title='Streamlit cheat sheet',
+ layout="wide",
+ initial_sidebar_state="expanded",
+)
+
+def main():
+ """
+ Main function to set up the Streamlit app layout.
+ """
+ cs_sidebar()
+ cs_body()
+ return None
+
+
+def img_to_bytes(img_path):
+ """
+ Converts an image to base64 encoded bytes.
+
+ Parameters:
+ img_path (str): Path to the image file.
+
+ Returns:
+ str: Base64 encoded image bytes.
+ """
+ img_bytes = Path(img_path).read_bytes()
+ encoded = base64.b64encode(img_bytes).decode()
+ return encoded
+
+
+# Define the function for the sidebar
+def cs_sidebar():
+ """
+ Populate the sidebar with various content sections.
+ """
+ # Display Streamlit logo with link
+ st.sidebar.markdown(
+ '''[](https://streamlit.io/)'''.format(
+ img_to_bytes("docs/examples/streamlit/streamlit.png")), unsafe_allow_html=True)
+
+ # Header and summary
+ st.sidebar.header('Streamlit cheat sheet')
+ st.sidebar.markdown('''
+Summary of the [docs](https://docs.streamlit.io/), as of [Streamlit v1.25.0](https://www.streamlit.io/).
+ ''', unsafe_allow_html=True)
+
+ # Installation information
+ st.sidebar.markdown('__Install and import__')
+ st.sidebar.code('$ pip install streamlit')
+ st.sidebar.code('''
+# Import convention
+>>> import streamlit as st
+''')
+
+ # Adding widgets to sidebar
+ st.sidebar.markdown('__Add widgets to sidebar__')
+ st.sidebar.code('''
+# Just add it after st.sidebar:
+>>> a = st.sidebar.radio(\'Choose:\',[1,2])
+ ''')
+
+ # Magic commands
+ st.sidebar.markdown('__Magic commands__')
+ st.sidebar.code('''
+'_This_ is some __Markdown__'
+a = 3
+'dataframe:', data
+''')
+
+ # Command line commands
+ st.sidebar.markdown('__Command line__')
+ st.sidebar.code('''
+$ streamlit --help
+$ streamlit run your_script.py
+$ streamlit hello
+$ streamlit config show
+$ streamlit cache clear
+$ streamlit docs
+$ streamlit --version
+ ''')
+
+ # Pre-release features
+ st.sidebar.markdown('__Pre-release features__')
+ st.sidebar.code('''
+pip uninstall streamlit
+pip install streamlit-nightly --upgrade
+ ''')
+ st.sidebar.markdown(
+ 'Learn more about [experimental features](https://docs.streamlit.io/library/advanced-features/prerelease#beta-and-experimental-features)',
+ unsafe_allow_html=True)
+
+ # Attribution and footer
+ st.sidebar.markdown('''
+
+
+This repository serves as a hub for various Cheat Sheets related to
+the field of Data Science (with Python). What sets these Cheat Sheets apart is their
+multi-dimensional approach to enhancing the learning experience.
+Each Cheat Sheet is made available in three distinct formats: **PDF**, **Streamlit**, and **Google Colab**.
+
+This triad of accessibility ensures that learners can engage with the content in
+a way that suits their preferences and learning style.
+
+
+**Content**
+
+
+| Topic | PDF | Streamlit | Google Colab |
+|---------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| python | | | |
+| numpy | | | |
+| pandas | | | |
+| matplotlib | | | |
+| scikit-learn | | | |
+
+> **Note**: The PDF format cheat sheets included here are authored by other contributors and have been used as sources of inspiration for the content presented.
+
+## More Information
+
+### What is a Cheat Sheet ?
+
+A cheat sheet is a concise and informative reference guide that provides
+quick and easy access to essential information or instructions about a specific topic.
+
+It's designed to help individuals quickly understand key concepts, commands, formulas,
+or procedures without having to search through lengthy documentation or resources.
+Cheat sheets are often used as handy reference tools for tasks that require familiarity
+with specific details or steps, such as programming languages, software applications,
+or academic subjects. They serve as a valuable aid for both beginners and experienced
+practitioners by condensing important information into a single, easily digestible format.
+
+### What is Streamlit ?
+
+
+
+
+[Streamlit](https://streamlit.io/) is an open-source Python library that simplifies and accelerates the
+process of creating interactive web applications for data science and machine learning projects.
+It allows developers, data scientists, and researchers to transform data scripts into shareable
+web applications quickly and with minimal effort.
+
+
+
+
+### What is Jupyter Notebook ?
+
+
+
+
+[Jupyter Notebook](https://jupyter.org/) is an open-source web application that provides
+an interactive and flexible environment for creating, sharing, and executing documents that contain live code, equations, visualizations, and explanatory text. It's widely used by researchers, data scientists, educators, and professionals
+to develop and present code-based projects, analyses, and reports.
+
+
+
+
+### What is Google Colab ?
+
+
+
+
+[Google Colab](https://colab.research.google.com/), short for Google Colaboratory, is a cloud-based,
+interactive development environment provided by Google
+that enables users to write, execute, and share Python code in a
+collaborative and convenient manner. It's particularly popular among researchers,
+data scientists, and educators for its ease of use and the fact that it doesn't
+require any setup or installation.
+
+
+
+## References
+
+* [Data Science Cheat Sheets - DataCamp](https://www.datacamp.com/cheat-sheet)
+* [Cheatsheets and Handouts - Matplotlib](https://matplotlib.org/cheatsheets/)
+* [Streamlit Cheat Sheet - Daniel Lewis](https://github.com/daniellewisDL/streamlit-cheat-sheet)
+
diff --git a/mkdocs.yml b/mkdocs.yml
new file mode 100644
index 0000000..17af1ae
--- /dev/null
+++ b/mkdocs.yml
@@ -0,0 +1,109 @@
+# Copyright (c) 2016-2022 Martin Donath